Apparatus for generating at least one diverse signal based on at least one aspect of at least two received signals

ABSTRACT

An apparatus for generating at least one signal based on at least one aspect of at least two received signals is provided. The apparatus comprises: a diverse antennae array of M antennae, where M is greater than or equal to two; at least one multiple-input and multiple-output capable transceiver in communication with each antenna in the diverse antennae array of M antennae; encoding circuitry capable of causing first data to be encoded; decoding circuitry capable of causing second data to be decoded; and processing circuitry capable of causing diversity combining, where the processing circuitry is in communication with the multiple-input and multiple-output capable transceiver, the encoding circuitry, and the decoding circuitry. In operation, the processing circuitry is capable of causing the apparatus to: receive at least two first signals, combine at least two of the at least two first signals, generate at least two second signals based on at least one aspect of the at least two first signals, and simultaneously transmit the at least two second signals. Additionally, the apparatus is configured such that at least one of the at least two second signals is capable of being received by a multiple-input capable node.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims priority to U.S.patent application Ser. No. 11/880,825, filed Jul. 23, 2007, which is acontinuation in part of and claims priority to patent application Ser.No. 09/878,789, filed on Jun. 10, 2001, issued as U.S. Pat. No.7,248,841 on Jul. 24, 2007, which are incorporated herein by referencein their entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates to MIMO devices, and more particularly toMIMO devices capable of being utilized in point-to-multipoint and meshnetworks.

BACKGROUND

The field of wireless communication networks has challenged implementerswith continuously discovered synergies, both positive and negative. Thesea of signaling has long grown from scattered and isolated sparks ofMorse code to the modern-day roar of intermingling transmissions. Thesimplicity of the directional link (Point to Point) was replaced by thebroadcast (Point to Multipoint) and is being replaced by the mesh(Multipoint to Multipoint) and even the relaying, multi-hop, interactivemesh; also, the continuous-transmission format is being replaced byshort and varying packets. The complexities of variations in real worldconditions—constantly changing topography, overlapping wave signals, andunpredictable and intermittent faults or blockages—all challenge theexisting methods and systems. Further description of some of theseproblems can be found in the parent application's text and will not berepeated here. This invention is concerned with the problems describedbelow.

PROBLEMS FORMING THE OPPORTUNITY FOR THE INVENTION

The primary problem solved through the invention is communication ofshort, intermittent packets, in particular Voice over IP (VoIP)communication signals, over communication networks subject tosignificant time-and-frequency co-incident (“co-channel”) interferencefrom other network users and external emissions. A secondary problemsolved through the invention is efficient physical routing of packetsover multiple network nodes, e.g., using multihop relay techniques, inorder to improve rate, robustness (immunity to interference andinformation warfare measures), reliability, and availability ofcommunications between Source and Destination nodes in the network. Atertiary problem solved through the invention is means for rapidconfiguration and scalability of transceiver capabilities in highlydynamic environments where the density and severity of interferers,numbers and capabilities/requirements of communication nodes, and natureof channel propagation may change rapidly and dynamically betweencommunication opportunities and/or over the course of a singlecommunication opportunity.

ADVANTAGES OF THE INVENTION

The invention provides the following capabilities and advantages:

-   -   Common, scalable transceiver blocks that can be implemented at        individual nodes in the communication network (allowing phased        addition of hardware and software without immediately rendering        obsolete the previous infrastructure).    -   Integration into a signal's waveform structure (“overhead        structure”) of overhead bits associated with point-to-point        links in the network, e.g., Transmit and Receive Node Addresses        (TNA's and RNA's), and then use of the resulting unified        waveform structure both to securely identify nodes attempting to        communicate with a receiver, and to develop linear combiner        weights that can extract those signals from co-channel        interference incident on that receiver (including interference        from other nodes attempting to communicate with that receiver).        This approach thereby eliminates bits needed for transmission of        TNA and RNA in headers attached to data packets transmitted over        each link in the network, as well as overhead needed for        transmission of pilot tones/sequences, training signals,        preambles/midambles, Unique Words, etc., typically used to train        receivers in the network; thus reducing the transmission        overhead and improving information-transmission efficiency.    -   Ability to further exploit overhead structure to increase        transmit power and data rate, or to allow same-rate        communication at reduced power to nodes in the network, thereby        regaining link capacity lost by that overhead structure, with        the most capacity regained at low receive        signal-to-interference-and-noise-ratio (SINR).    -   Optional integration of overhead bits associated with multipoint        routes in the network, e.g., Source and Destination Node        Addresses (SNA's and DNA's) into the waveform structure used for        adaptation of the communication transceivers (“overhead        structure”), further reducing bits needed for transmission of        SNA and DNA in headers attached to data packets transmitted in        multihop networks, and allowing the use of macrodiverse relay        networks in which data is coherently transmitted over multiple        geographically separated nodes in a network.    -   Rapid (single packet) node detection/discovery and join/leave        algorithms, allowing individual transceivers to enter or exit        the network quickly to exchange traffic, update security codes,        etc., and to allow rapid and/or ad hoc configuration of the        network as users encounter dynamic changes in multipath, fading,        or interference.    -   Information assurance (IA) measures, e.g., antijamming and        antispoofing capability, at the node and network level,        including spreading means that defeat denial-of-service measures        in which the frequencies and/or time periods containing        synchronization and training bits are selectively jammed by an        adversary.    -   Adaptive power management and cyclic feature reduction at node,        link, and network levels, in order to minimize transmitted power        and/or detectable features of emitters in the network.    -   Extreme low complexity (<200 kcps DSP software operations, <30        Mcps ASIC or FPGA coreware operations) for communications        commensurate with VoIP communication, allowing maintenance of a        collaborative networking information commensurate with        pedestrian networking applications.

Collaborative communication applications that can be additionallyhandled by these transceivers include the following:

-   -   Distributed Kalman state circulation to enable wide-baseline        network geolocation algorithms.    -   Internode channel measurement and range/timing/carrier offset        estimation algorithms used to enable wide-baseline network        geolocation algorithms.    -   Collaborative interference avoidance methods during transmission        and reception operations, e.g., allowing wide-area        communications in presence of jammers in military communication        systems, or incumbent broadcast emitters in commercial        communication systems (e.g., 802.22).    -   Collaborative communication over long-range to out-of-theatre        nodes, e.g., reachback nodes in military communication networks,        or LEO/MEO/GEO satellites in commercial satellite communication        networks.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is depicted in the Figures below.

FIG. 1 illustrates a Point-To-Point or PTP network; each pentagonindicates a node, or transmission and reception (a.k.a. transceiver)station, and each arrow indicates a link along which communication flowsbetween nodes.

FIG. 2 illustrates a Point-To-Multipoint network. The large pentagonindicates a Base Station (BS) capable of communicating with manyindividual Subscriber Units (SU), indicated by the small pentagons.

FIG. 3 illustrates a more complex PMP network with multiple BS and SUnodes, and multiple links. The solid lines indicate one diversitychannel and the dotted lines a second diversity channel.

FIG. 4 illustrates a multipath environment, where a single BS with acomplex antenna radiates multiple beams to a single SU (the car),wherein the beams also arrive from reflections off the surroundingfeatures.

FIG. 5 illustrates a null-steering effort by a single node (Item 101),possessing at least three antennae, which is capable of directingtowards two unintended recipients (Item 102) null transmissions (Item105) and directing a focused beam (Item 104) towards an intendedrecipient (Item 103).

FIG. 6A illustrates a more complex multipoint network, where the BSscommunicate with each other and with individual SUs, even if a BS to BSlink may risk interfering with communications between the BSs and aparticular SU.

FIG. 6B illustrates a more effective use of the existing diversity ofchannels, whereby the BS to BS signal passes through a (possiblemultiplicity of) channel(s) from one BS to the intervening SUs thence tothe second BS.

FIGS. 7A and 7B illustrate a capacity problem that may arise with aprior art PMP network when a new node attempts to enter and existingnodes are not capable of dynamically adapting diversity channels to formthe new subsets. Although nodes C and E can readily talk with D, bysubstituting their direct link to each other for intervening links withD, nodes D, A, and B, being limited to 3 existing channels, cannot adaptto connect with each other by dropping either E or C depending ontraffic needs.

FIG. 8 illustrates a Time-Division Duplex communications protocol,whereby alternating uplink and downlink, or transmission and reception,slices of network activity take place at a node.

FIG. 9 illustrates an asymmetric network where nodes C and D havegreater capacity than nodes A and B, which in turn have greater capacitythan node D, but where the network cannot dynamically allocate thiscapacity to meet signal density needs to differing subsets of nodes.

FIG. 10 illustrates a multipath network, where transmissions betweennode 1 and node 2 are both direct and reflected off environmentalfeatures both near and far.

FIG. 11 illustrates a data flow diagram in a PTP MIMO link, where signalflows from one CODEC in node 2 through all of node 2's antennae, thenceinto all of node 1's antennae, and finally into one CODEC of node 1.Existing multipath potential of either or both reflectors, and dynamicallocation of less or more of the possible diversity modes, is ignored.

FIG. 12 illustrates a physical PTP multipath, consisting of one directand two reflective links, using all of node 1's antennae andtransceivers, and all of node 2's antennae and receivers.

FIG. 13A illustrates a network of MIMO-capable nodes, that is, nodeswith multiple antennae, multitone transceivers with DSP capability (FIG.13B), wherein the network has two subsets with preferentially reciprocaluplinks and downlinks and diversity channel capacity between thesubsets.

FIG. 14 illustrates MIMO-capable nodes wherein each node transmits andreceives signal energy during alternating time slots (or sequences oftime slots in TDD-TDMA systems).

FIG. 15 illustrates more details of the MIMO-capable node, including onthe receiving side: receiving spatially and/or polarization diverseantennae in a multiple antennae array (Item 110), a vector OFDMtransceiver switch (Item 112), a LNA bank (Item 113), an AGC (Item 114),a Frequency translator (Item 115), a LOs ((Item 116), an ADC bank (Item117), a MultiTone Demodulator Bank (Item 118), means for mappingreceived data over each diversity channel, on each frequency channel andreceive time slot (Item 119), and a transceiver controller (Item 120);and including on the transmitting side means for mapping transmitteddata for each frequency channel and transmit time slot (Item 121), aMultiTone Modulator bank (Item 122), a DAC bank (Item 123), thetransceiver switch, a PA bank (Item 124), and transmitting spatiallyand/or polarization diverse antennae in a multiple antennae array (Item125) which may be distinct from those used in receiving (Item 110).

FIG. 16 illustrates a MIMO-capable network of the preferred embodiment,wherein an originating transceiver Node 2 in an uplink transmit set(Item 200) distributes a signal through multiple antennae (Items 204Aand 204B), which goes over the channel matrix of diversity channelsavailable (Items 206A,B,C, and D) to a uplink receive set node, whichreceives the signals over its multiple antennae and combines them (Items208A and B) to the desired recipient (Item 202); leaving all othertransmissions and channel diversity available (Item 212) for othernetwork communications.

FIG. 17 illustrates a PTP MIMO node layout employing TDD, but one usinga guard-time prefix (Item 220) and a 10 ms content frame period (Item222), where the uplink Tx for Node 2 is at the time for the Uplink Rxfor Node 1.

FIG. 18 illustrates a MIMO network in a star topology, where the uplinkrelay node (Node 1, Set 2) talks to 4 edge nodes (Nodes 1-4, set 1) withdistinct channels for uplinks and downlinks.

FIG. 19 illustrates a complex network topology with multiple BSs (Items260, 262), SUs (Items 264, 266, 268, and 270), and a potentiallyinterfering non-network node (Item 275), with transmissions amongst thenetwork (280, 282, 284, 290, 292) competing with transmissions fromoutside the network which are perceived as interference at BS 1 (Item260) and SU1 (Item 270).

FIG. 20 illustrates a MIMO network in a ring configuration withreciprocity at the network level.

FIG. 21 illustrates the means for generating the pilot tone mask withnetwork code mask pseudodelay (Item 170), originating node index maskelement (Item 172), and Recipient node index mask element (Item 174)being combined by two element-wise MUX units (Items 176, 178), to createthe final pilot tone signal (Item 180).

FIG. 22 illustrates a time-frequency mapping pattern of an acquisitionchannel (Item 340, 342), control channel (Item 344), 32 Data channels(Item 346, not shown for D1 through D31), with cyclic prefixes (Items322, 318, 314) and a guard time (Item 310) with 100 μs acquisitionsymbols (Item 320), control symbols (Item 316), and then 32 data symbols(Item 312, again not shown for D1-D31).

FIG. 23 illustrates an alternative time-frequency mapping pattern forhigh-mobility situations, where the acquisition, control, and dataelements are repeated (Items 362, 366, and 370), but the number ofdata-bearing channels is halved to allow the duplication within the samebandwidth and time.

FIG. 24 illustrates one embodiment of the MIMO transceiver, with the RFfeeds (Item 130), the Frequency channel bank (Item 132), the mappingelement (Item 134), the Multilink Rx weight adaptation algorithm (Item136), the Multilink diversity Rx weights combining element (Item 138),the equalization algorithm (Item 140) and Delay/ITI/pilot gating removalbank (Item 142), symbol decoder bank (Item 144), multimode powermanagement, symbol coding assignment algorithm element (Item 146), thesynchronization elements (Items 162, 164), and T/R comp. Algorithm (Item156) and element (Item 158), and multilink diversity distribution of TxGains element (Item 152).

FIG. 25 illustrates in more detail the frequency translator, detailingthe Band Pass Filters, element wise multiplier, sinusoids, SAW BPF, andLPF elements.

FIGS. 26 and 27 illustrate tone-mapping to frequency bins approaches forlow and high mobility situations, respectively.

FIG. 28 illustrates in more detail the implementation of the Delay/ITIpreemphasis, pilot-gating bank, tying the details of FIG. 21 into themapping element (Item 474), the Trellis encryption and encoding element(Item 472), the pilot signal and information signal MUX (Item 480),leading to the final Tx data symbols signals (Item 490).

FIGS. 29 and 30 illustrate the antennae feeds (502, 512) acrossdiversity modes and multilinks through the Multilink AdaptationAlgorithm element (500) to and from the Link and multitone symboldistributor/combiner inputs and outputs (506, 508; and 516, 518respectively).

FIG. 31 illustrates the incorporation of the preferred embodiment of theMultilink LEGO gain adaptation algorithm and element (Item 530) into thediversity combining and distribution elements of the MIMO transceiverhardware.

FIG. 32 illustrates the LEGO optimization function for a target capacityobjective β.

FIG. 33 illustrates the network LEGO optimization function for a networkcontroller, using constraint R_(1q) and target objective β, to determinefor the network or node which should be incremented or decremented.

FIG. 34 illustrates two nodes using dynamic, feedback driven informationfrom transmissions and receptions to perform a particular LEGOoptimization, involving observed interference power from non-subset ornon-network BSs (528) or SUs (580).

FIGS. 35 and 36 illustrate the FFT-based LS algorithm used in thepreferred embodiment that adapt {W₁(k, 1; n₂, n_(i))} and {w₂ (k, 1; n₁,n₂)} to values that minimize the mean-square error (MSE) between thecombiner output data and a known segment of transmitted pilot data.

FIG. 37 illustrate the FFT-based LS algorithm used in the preferredembodiment for the normalized MMSE or, in an alternative embodiment, theGauss-Newton algorithm.

FIGS. 38A and 38B illustrate a MIMO network with null-steering andpilot-tone transmissions, with the overall transmission shown as theExtraction SINR, and the mask-fitted transmissions perceived at 702A and702B which correctly account through the imposed pilot pseudodelay forthe intended transmission peaks.

FIGS. 39 and 40 illustrate alternative topological layouts for properuplink receive and uplink transmit subsets with links and expectedattenuation.

FIG. 41 illustrates, for a TDD MIMO network as in the preferredembodiment, an algorithm for any node entering the network.

FIG. 42 shows MIMO-capable hardware transceiver means for, and theprocessing steps performed, in the baseline two-channel transceiver usedin the invention.

FIG. 43 shows enhanced MIMO-capable hardware means for, and theprocessing steps in combining, baseline two-channel transceivers toprovide additional degrees of freedom (receive combining and transmitdistribution) in larger networks.

FIG. 44 shows an exemplary MIMO networking routing scenario in whichfour transceivers with four spatial degrees of freedom are used to routedata around a four-node network in the presence of a strong jammer.

FIG. 45 shows the baseline symbol and timing structure for eachtransmission, when transceivers are operated in a symmetrictime-division duplex (TDD) multinode network.

FIG. 46 shows an exemplary deployment and instantiation of the inventionin a communication over a TDD mesh network topology

FIG. 47 shows the baseline symbol and timing structure for eachtransmission, when transceivers are operated in a ad-hoc multinodenetwork.

FIG. 48 shows an exemplary deployment and instantiation of the inventionin a communication over an ad hoc mesh network topology.

FIG. 49 shows multiport PHY transmit operations and processing performedat transceivers using the invention.

FIG. 50 shows multiport PHY receive operations and processing performedat transceivers using the invention.

FIG. 51 shows multiport PHY receive adaptation operations and processingperformed in the primary embodiment of the invention.

FIG. 52 shows receive packet detection, address association, and linkSINR estimation processing performed in the preferred embodiment of theinvention.

FIG. 53 describes transmit adaptation algorithms operations andprocessing performed in the preferred embodiment of the invention.

OBJECTS

Resolving many of the prior art's weaknesses by enabling true,opportunistic, MIMO networks is one of the principle objects of theinvention. Creating a truly adaptive, flexible, multi-protocol wirelesselectromagnetic communications network is a second of the principleobjects of the invention. A third is to simultaneously resolve theinterplay between transmission power and network capacity by consideringand using the interplay between one local node's transmissions as asignal and other nodes' reception of the same as either a signal (if thereceiving node is an intended target) or as environmental noise (if thereceiving node is an unintended target).

A secondary objective under this third objective is to provide methodswhich improve signal quality received by a targeted recipient node whilesimultaneously reducing interference energy received by other untargetedrecipient nodes, so as to enable improved capacity amongst existingnodes, adding more nodes, increasing coverage area, and improvingcommunications quality, or any sub-combination thereof.

Another secondary objective under this third objective is to provide anadaptive method which accounts for multipath interaction amongst thenodes and network, and minimizes unwanted effects while maximizingpotential useful effects thereof. Another secondary object under this isto provide improved load balancing amongst nodes and communication pathsor links within the network with a minimum of overall control.

Another secondary object under this is to enable improved access to newnodes to the network.

Another secondary object under this is to enable multiple, competing,yet cooperating sub-networks that are mutually and automaticallyadaptive and responsive.

A second of the principal objects of the invention is to simultaneouslyresolve the interplay between local optimization, which demands detailedconsideration of the immediate environmental details that affect eachlink between that node and others over which communications are flowing,and global optimization, which demands a minimum of control informationbe exchanged across the network and amongst the nodes in lieu ofotherwise usable signal capacity.

A secondary object under this is to use the reception of signalinformation from other nodes, both those targeting the recipient andthose not targeting the recipient, to enhance both reception andtransmission quality to and from the receiving node while minimizing theexplicit and separate feedback signals that must be exchanged amongstthe nodes and network.

Another secondary object under this is to provide methods foroptimization that can use or be independent of antenna array geometry,array calibration, or explicit feedback control signals from othernodes, whether the same are continuous, regular, or reactive toenvironmental changes affecting the link between the receiving node andthe other nodes.

A third of the principal objects of the invention is to maximize thecommunications capacity and minimize the power usage both locally andglobally across the network for any given set of hardware, software, andprotocols.

A secondary object under this is to provide higher content throughput inunderloaded networks, thereby providing faster perceived access orusage.

A secondary object under this is to provide higher reliability for anygiven hardware and software implementation.

A fourth of the principal objects of the invention is to provide amethod for network optimization that can be extended to mixed networks,whether such mixing is amongst wireless and fixed links, or amongstelectromagnetic spectra, or amongst types of nodes (BSs, dumb terminals,single- or limited-purpose appliances, or human-interactiveinput/output), and across both access schema and communicationsprotocols with a minimum of particularization.

A fifth of the principal objects of the invention is to providerelatively simple and powerful methods for approximation which enableimprovement that rapidly converges to the best solution for anyoptimization.

A secondary object under this is to provide a computationally efficientmechanization for cross-correlation operations that takes maximaladvantage of multiport signals on particular single channels.

A sixth of the principle objects of the invention is to maximize the useof local information and minimize the use of global information that isrequired for approximation and approach to the best solution for anyoptimization.

SUMMARY OF THE INVENTION

The multiple-input, multiple-output (MIMO) network approach summarizedhere can incorporate as lesser, special cases, point-to-point links,point-to-multipoint networks, and disjoint (e.g., cellular)point-to-point links and point-to-multipoint networks. It can also beapplied to spatial, temporal, or frequency-based access schemes (SDMA,TDMA, FDMA) employing combinations of spectral, temporal, spatial orpolarization diversity, and to fixed, mixed, and mobile communications,as its focus is on the network in context rather than on the signaldifferentiation methodology, access determinations, or basing structure.One key to this approach is employment of spatially (or more generallydiversity) adaptive transmit and receive processing to substantivelyreduce interference in general multipoint links, thereby optimizingcapacity and/or other measures of network quality in multiply connectednetworks. A second key to this approach is the minimization of secondaryconsequences of signaling, and a second is using internalized feedback,so that the signaling process itself conveys information crucial to theoptimization. Rapid, dynamic, adaptation reactive to the changingenvironment and communications within and surrounding each node and theentire network is used to promote both local and global efficiency.Unlike Varanesi, the feedback is neither limited to BSs only, noreffectively independent of the continual, real-world, signal and networkenvironmental adaptation.

Instead of avoiding diversity, or fighting diversity, the presentembodiment of the invention exploits and makes use of spectral,temporal, polarization, and spatial diversity available at each node, aswell as and route (location based) diversity provided over the networkof nodes. The network of nodes uses MIMO-capable nodes, that is, nodeswith multiple antennae, multitone transceivers and preferentiallyreciprocal uplinks and downlinks (FIG. 13A and FIG. 13B). In thepreferred embodiment each node transmits and receives signal energyduring alternating time slots (or sequences of time slots in TDD-TDMAsystems) (FIG. 14); has a spatially and/or polarization diversemultiple-antenna array, a vector OFDM transceiver that downconverts, A/Dconverts, and frequency channelizes data induced on each antenna (orother diversity channel) during receive time slots, and inversechannelizes, D/A converts, and upconverts data intended for each antenna(or diversity channel) during transmit time slots; linearly combinesdata received over each diversity channel, on each frequency channel andreceive time slot; redundantly distributes data intended for eachdiversity channel, on each frequency channel and transmit time slots;and computes combiner and distributor weights that exploit the,narrowband, MIMO channels response on each frequency channel and timeslot (FIG. 15).

The concept of a ‘diversity channel’ is introduced to permit adistinction to be made between “channels” that data is redundantlydistributed across during receive (or transmit) operations, from“channels” that data is transported over (e.g., frequency channels ortime slots). Data is redundantly transported over diversity channels,i.e., the same data is transported on each diversity channel withweighting determined by the methods described in detail below, whileindependent data is generally transported over the second flavor ofchannel. Effectively exploiting available diversity dimensions, thepresent embodiment of the invention can maximize its ability to attainRank 2 capacities, since multiple redundant transmissions can be madeover the plurality resources, whether that plurality comes fromdifferent frequencies, multipaths, time slots, spatially separableantennae, or polarizing antennae. This is distinct from prior artapproaches which required multiple redundant transmissions over aplurality of frequencies to attain Rank 2 capacities. Because the signalflow between nodes is not limited to a particular dimension ofsubstantive differentiation the preferred embodiment of the network canat every point in time, and for every node in the network, exploit anyand all diversity opportunities practicable and attainable for thenetwork's communication channels.

The signal flow between the multiplicity of nodes in the networkcomprises a multiplicity of information channels, emanating from andbeing received by a set of antennae at each node (FIG. 16). The physicalchannel flow in a network with M(n) diversity channels (e.g., M(n)antennae per node, at each node n in the network) means that for eachtransmitting node's pair of antennae, as many as M distinguishablereceptions are feasible at each receiving node it can communicate with(FIG. 15, M=4, for a PTP link example).

The preferred embodiment performs complex digital signal manipulationthat includes a linear combining and linear distribution of the transmitand receive weights, the generation of piloting signals containingorigination and destination node information, as well asinterference-avoiding pseudorandom delay timing (FIG. 17), and bothsymbol and multitone encoding, to gain the benefit of substantiveorthogonality at the physical level without requiring actual substantiveorthogonality at the physical level.

The network is designed such that a subset of its nodes are MIMO-capablenodes, and that each such node can simultaneously communicate with up toas many nodes in its field of view as it has antennae. The network isfurther designed such that it comprises two or more proper subsets, eachproper subset containing members who cannot communicate directly withother members of the same proper subset. So if the network containedonly two proper subsets, First and Second, the members of First couldtransmit only to, and receive only from, the members of Second, and themembers of Second could transmit only to, and receive only from, themembers of First. Independent information is then transmitted from everymember of First and is independently processed by each member of Second.(See FIG. 18, exemplifying one such topology, a ‘Star’ topology; andFIG. 19, exemplifying another such topology.)

A non-MIMO-capable node may belong to any subset containing at least oneMIMO-capable node that has at least one antenna available to thatnon-MIMO-capable node that has the non-MIMO-capable node in its field ofview.

Diversity channels, rather than antennae, limits the number of non-setmembers a MIMO-capable node may communicate with simultaneously, thatis, the number with which it may hold time/frequency coincidentcommunications. Also, this limiting number is a function of number ofusers attempting to communicate over the same diversity channels—userson different time-frequency channels do not affect this limit. Thus anode with 128 time frequency channels (8 TDMA time slots×16 FDMAfrequency channels) and a 4 antennas (4 diversity channels pertime-frequency channel) can support up to 4×128=512 links, to as manyusers. If the internode channel response to a given user has rank 1(e.g., if antennas are on same polarization and multipath is absent),then only a single link can be established to that user on eachtime-frequency channel, e.g., 128 separate links (one on eachtime-frequency channel) in the example given above. Higher internodechannel rank allows more channels to be established; for example ifnodes are polarization diverse, then the internode channel response hasrank 2 and 256 channels (2 per time-frequency channel) can beestablished. The MIMO channel response equation determines power on eachchannel—depending on needs of network and pathloss to user, some or mostof channels nominally available may be turned off to optimize theoverall network capacity.

A significant element is that the diversity channel distribution neednot be equal; one recipient node may have half the channels, if thetraffic density requires it, while the transmitting node may divide itsremaining channels evenly amongst the remaining nodes. Therefore themore users that have rank 2 or better capacity, the greater theavailable channels for those who have only capacity 1. This supportsincremental optimization as improvement for the network is not dependentupon global replacement of every lesser-capacity node, but results fromany local replacement.

The preferred embodiment details the means for handling the twoalternative cases where the interference is, or is not, spatially whitein both link directions, the means for handling interference that istemporally white over the signal passband. Preferentially, each link inthe network possesses reciprocal symmetry, such that:

H ₁₂(k;n ₁ ,n ₂)=H ^(T) ₂₁(k;n ₂ ,n ₁),  EQ. 1

Where H₁₂ (k;n₁,n₂) is the M₁(n₂)×M₂(n₁) MIMO transfer function for thedata downlinked from node 1 to node 2 over channel k, less possibleobserved timing and carrier offset between uplink and downlink paths,and,H₂₁(k;n₂,n₁)s the M₂(n₂)×M₁(n₁) MIMO transfer function for the datauplink from node 2 to node 1 over channel k, and where ( )^(T) denotesthe matrix transpose operation.

In the preferred embodiment, this is effected by using the TDD protocol,and by sharing antennas during transmit and receive operations andperforming appropriate transceiver calibration and compensation toremove substantive differences between transmit and receive systemresponses (this can include path gain-and-phase differences after thetransmit/receive switch, but does not in general require compensation of[small] unequal observed timing and carrier offset between uplink anddownlink paths).However, simplex, random-access packet, and otheralternative methods are also disclosed and incorporated herein.

The network is further designed such that at each MIMO-capable node nwith M(n) antennae, no more than M(n) other actively transmitting nodesare in node n's field of view, enabling node n to effect a substantivelynull-steering solution as part of its transmissions, such that each nodebelonging to a downlink receive set can steer independent nulls to everyuplink receive node in its field of view during transmit and receiveoperations, and such that each node belonging to an uplink receive setcan steer independent nulls to every downlink receive node in its fieldof view during transmit and receive operations.

The preferred embodiment also has means for incorporation of pilot dataduring transmission operations, and means for computationally efficientexploitation of that pilot data during subsequent reception operations.This is to enable transmitting nodes to unambiguously direct informationto intended recipient nodes in the network; to enable receiving nodes tounambiguously identify information intended for them to receive; toenable nodes to rapidly develop substantively null-steering receiveweights that maximize the signal-to-interference-and-noise ratio (SINR)attainable by the link, to enable nodes to reject interference intendedfor other nodes in the network, to enable nodes to remove effects ofobserved timing offset in the link, and to enable the nodes and networkto develop quality statistics for use in subsequent decoding, errordetection, and transmit power management operations.

The preferred embodiment prefers a network designed to create andsupport a condition of network reciprocity, where the uplink anddownlink criteria are reciprocal at the network level. (FIG. 19). Thepresent form of the invention further exploits the reciprocity to attainboth local and global optimization, of both capacity and power, throughlocally enabled global optimization of the network (LEGO).

LEGO is enabled by exploiting substantive reciprocity of the internodechannel responses, together with appropriate normalization of transmitpower measures, to design uplink and downlink network quality metricsD₂₁ (W₂,G₁) and D₁₂(W₁,G₂) that satisfy network reciprocity property:

D ₁₂(W,G)=D ₂₁(G*,W*)  EQ. 2

where (W₂,G₁) and (W₁,G₂) represent the receive and transmit weightsemployed by all nodes in the network during uplink and downlinkoperations, respectively. If equation 1 holds, then equal networkquality can be achieved in each link direction by setting G₁=W₁* andG₂=w₂*, such that each node use the receive combiner weights as transmitdistribution weights during subsequent transmission operations, i.e.,the network is preferentially designed and constrained such that eachlink is substantially reciprocal, such that the ad hoc network capacitymeasure can be made equal in both link directions by setting at bothends of the link:

g₂(k,q)∝w₂*(k,q) and g₁(k,q)∝w₁*(k,q)

where {g₂(k,q), w₁(k,q)} are the linear transmit and receive weights totransmit data d₂(k,q) from node n₂(q) to node n₁(q) over channel k inthe downlink, and where {g₁(k,q),w₂(k,q)} are the linear transmit andreceive weights used to transmit data d₁(k,q) from node n₁(q) back tonode n₂(q) over equivalent channel k in the uplink; thereby allowing Eq.1 to be satisfied for such links.

The invention further iteratively optimizes network quality (as definedby D₁₂ and D₂₁) over multiple frames, by first adapting combiner weightsto locally optimize link (and therefore network) performance duringreceive operations, and then using Eq. 1A and the reciprocity property(Eq. 1) to further optimize network quality in the reverse directionover subsequent transmit operations.

The invention further improves on this approach by using Eq. 1 to scaleeach transmit vector, based on a partial linearization of the networkquality metrics, to either minimize the total transmit power in theentire network subject to a network quality constraint, preferentiallycapacity, or maximize network quality, preferentially capacity, subjectto a total transmit power constraint. This constraint is defined andmanaged as a control parameter that is updated by the network. The totaltransmit power at a given node is then reported as an output to thenetwork.

By using target criteria such as (1) for a cellular network, a max-mincapacity criterion subject to a power constraint, or (2) for a wirelessLAN, a max-sum capacity that is subject to a power constraint, and usingsimple comparative operations in feedback for the network to optimizetowards those criteria, this invention enables flexibility and stabilityfor any given hardware and software combination that underlies awireless electromagnetic communications network and improves, for theentire network and at each particular node thereof, the communicationcapacity and power requirements. Furthermore, the present form of theinvention does not ignore but rather directly addresses and resolvesboth the overhead vs. content and the power vs. capacity conundrumswhich otherwise limit present-day state of the art approaches tooptimization. It does this using the experienced environment as part ofthe direct feedback, rather than requiring additional controlinformation or signaling that reduces content capacity.

When combined with the substantively null-steering approach describedhere, which helps to minimize the generated noise from all other signalssent from a node, the network power requirement for clear communicationdrops as the links effectively decouple; that is, the ‘other’ channels,since they are being null-steered, do not form part of the backgroundnoise against which the intended signal's power must be boosted to beaccurately received by the intended recipient. (See FIG. 7.)

The LEGO power optimization and null-steering then feed back(reciprocally) into the requirements for the network and network'shardware at each node, inasmuch as the minimization of unused andunintentional interaction (or interference) reduces the precision andpower necessary for frequency and other differentiation means at thenode's transceiver, and reduces the number of antennae in each array byincreasing the effective bandwidth within each multipath channel, byreducing the amount of bandwidth, frequency, time, or channel, or all ofthe above, that must be devoted to error avoidance or correction. Thatin turn simplifies the codec and other element designs for each node andlowers the cost of the transceiver front-ends.

The preferred embodiment of the invention employs a network offully-adaptive PHY-IA MIMO Network Capable Transceivers, in which eachtransceiver implementing an upper PHY that performs transmit and receiveTRANSEC, node signaling/detection protocol, transmit/receivebeamforming, and receive-side node discovery and adaptation algorithms,and a lower PHY that can meet the needs of intermittent, burst packetcommunications such as VoIP.

Communication between source and destination nodes, defined by uniquetwo-hex port addresses comprising the source and destination nodeaddresses (SNA's and DNA's) for the packet transmission, is accomplishedby routing traffic packets over at least one and possibly several routesor collections of sequential links between the source and destinationnode. Means for partitioning traffic data into individual data packetsat the source node, and collecting data packets into traffic packets atthe destination node, are accomplished in this invention using existingnetwork routing protocols. For each combination of a transmitting node,a receiving node, and a communication channel (diversity link), theunique link address and identifying transmit node address and receivenode address for the respective nodes are used as part of the messagingcontext.

Combining packet-specific, structural and origin/destination networkinformation into a unified overhead allows implementation of orthogonaltransformations of that overhead, through the use of specificpower-of-two integer number of lower PHY (LPHY) symbols (which arepreferably an OFDM waveform or PAM signal); and using a unique andidentifying link address for each node-to-node link currentlyinstantiated, which incorporates source, destination, and channel rankinformation, enables informational efficiency for short transmissionswhere otherwise structural and routing information might overweighcontent, e.g. in each packet. Working within bounds (time intervals,frequency ranges, or transmission strengths) that guard againstintrasystemic interference, the use of MIMO transformations andreciprocity-based pilot or signal weighting calculations for the correctweighting of signals transmitted and received, enables the individualpackets and messages to adapt, in a bottom-up, flexible, and responsivefashion to the real-world dynamics of a continuously varying EM flux.Using adaptively-derived diversity weighting, the method and system canrapidly take advantage of reciprocity between each node pairings'transmit and receive channels to distinguish the desired signal from thegeneral noise and potential interference. Upon RF reception at any node,downconversion and ADC operations on the diversity channels passes theincoming signal(s) through a set of inverting transformations that, forthe desired incoming signal(s), strip off known structural elements andcontinuously updates the combiner weights to reflect the dynamicallyvarying environmental and signal context, thereby continuously matchingnecessitated signal and waveform transformations to the environmentaland signal effects and sources. By successive iterations of the transmitand receive adaptation algorithm each node can have its transceiveradapt its multiport combiner and distribution weights to the eigenmodes(left and right eigenvectors) of their MIMO internode channel response,so that the resultant fully adaptive link can approach the Shannoncapacity of the MIMO communication channel, regardless of the rank ordistribution of the eigenvalues of that channel.

In addition, the fully adaptive system provides an automatic powercontrol mechanism (LEGO Algorithm) that can be used to maximize capacity(high throughput applications) or minimize transmit power (LPDapplications), depending on the requirements of the system at any pointduring a mission.

The resultant network is able to pass data with high spectral efficiencyrelative to non-MIMO networks, or to meet specified packet transmissionrates at much lower power levels relative to non-MIMO networks, due toits ability to pass data over multiple time-and-frequency coincidentlinks and routes, and to exploit the much lower pathloss betweenintermediate nodes in the network. Moreover, the network is able toprovide this performance in the complete lack of any opportunisticmultipath (although that multipath can be exploited if it is available).

DETAILED DESCRIPTION OF THE DRAWINGS Glossary And Definitions

ACK Acknowledgement ADC Analog-to-Digital Conversion ADSL AsynchronousDigital Subscriber Line AGC Automatic Gain Control BS Base Station BERBit Error Rate BW Bandwidth CBR Committed Bit-Rate service CDMA CodeDivision Multiple Access CE&FC RWA Computationally Efficient AndFast-Converging Receive Weight Algorithm CMRS Cellular Mobile RadioSystems CODEC Encoder-decoder, particularly when used for channel codingCPU Central Processing Unit CR Channel Reciprocity DAC Digital-to-AnalogConversion DEMOD Demodulator DMT Digital MultiTone, DSL Digital SignalLoss DMX De-multiplexer DOF Degrees of Freedom DSP Digital SignalProcessing EDB Error-Detection Block EEPROM Electronically Erasable,Programmable Read Only Memory FDD Frequency Division Duplex FDMAFrequency Division Multiple Access FFT Fast Fourier Transform(s) FPGAFreely Programmable Gate Array GPS Global Positioning Satellites GSMGlobal System for Mobile Communications LEGO Locally Enabled GlobalOptimization LMS Least Mean-Square LNA Low Noise Amplifier LSLeast-Squares (An alternative form can be ‘matrix inversion’) MAC MediaAccess Control MGSO Modified Gram-Schmidt Orthogonalization (mostpopular means for taking QRD) MOD Modulator MIMO Multiple-Input,Multiple-Output MMSE Minimum Mean-Square Error MSE Mean-Square Error MTMultitone MUX Multiplex, Multiplexer NACK Negative acknowledgement &request for retransmission NAK Negative Acknowledgement OFDM OrthogonalFrequency Division Multiplexing PAL Programmable Array Logic PDAPersonal Data Assistant PHS Personal Handiphone System PHY Physicallayer PMP Point-to-Multipoint (An alternative form can be ‘broadcast’)PSTN Public Switched Telephone Network PSK Phase-Shift Key π/4 QPSK(pi/4) - Quadrature Phase Shift Key π/4 DQPSK (pi/4) - DigitalQuadrature Phase Shift Key PTP Point-to-Point QAM Quadrature AmplitudeModulation QoS Quality of Service QRD Matric {Q, R} decomposition (see,MGSO) RF Radio Frequency RTS Request To Send, recipient ready fortraffic SDMA Spatial Division Multiple Access SINR Signal to NoiseRation (An alternative form can be S/N) SOVA Soft-Optimized, ViterbiAlgorithm SU Subscriber Unit TCM Trellis-Coded-Modulation TCP/IPTransmission Control Protocol/Internet Protocol TDMA Time DivisionMultiple Access TDD Time Division Duplex T/R Transmit/Receive (alsoTx/Rx) UBR Uncommitted Bit-Rate (services) ZE-UBR Zero-error,Uncommitted Bit-Rate (services)

Groundwork: the Network as a Dynamic Connected Set

A network is generally viewed as the combination of a set of nodes(where transmissions originate and are received) and the connectionsbetween those nodes through which the information is flowing. FIGS. 1A,1B, and 1C are graphical representation of a simple network of fivenodes (A through E) and a varying number of channels, indicated by thelines drawn between pairs of nodes. In FIGS. 1A and 1C, all five nodesare active and able to communicate with all or most of their neighbors.In FIG. 1B, node D is inactive and unable to communicate. The two-stepchannel, from C to D and from D to E, in FIG. 1A is replaced by aone-step channel from C to E in FIG. 1B, and co-exists with the two-stepchannel in FIG. 1C. A connection between any two nodes without anyintervening nodes is also known as a ‘link’.

Because each node may both transmit (send) and receive, and because theconnections amongst the set of nodes may change over time, the networkis best thought of as a dynamic structure, i.e. one that is constantlyshifting yet which still occupies the same general ‘space’ in thecommunications world. While traditional broadcast networks, or PTP orPMP networks generally tried to ‘fix’ at least the originating node, aMIMO network begins with the presumption that the communications aredynamically allocated amongst the nodes and throughout the network. Inthe present embodiment of the invention, diversity in spatial, spectral,temporal, or polarization attributes of the potential channels are notseen as variations that must be controlled or limited, but asopportunities for enhancing performance.

Limitations of Existing Art

The approaches currently described in the field, especially in Raleighand Cioffi, G. Raleigh, J. Cioffi, “Spatio-Temporal Coding for WirelessCommunications,” in Proc. 1996 Global Telecommunications Conf., November1996, pp 1809-1814), and in Foschini and Gans (G. Foschini, M. Gans, “OnLimits of Wireless Communication in a Fading Environment When UsingMultiple Antennas”, Wireless Personal Comm., March 1998, Vol. 6, Nol. 3,pp. 311-355), require additional hardware at each node comprising oneend of a channel per diversity path to exploit that diversity path. Thiscreates a geometric growth in the hardware complexity for eachparticular node, and a linear growth in cost for each diverse pathexploited by a given network, that rapidly renders any networkattempting to exploit such diversity uneconomic. Moreover, such anapproach ‘muddies its own stream’ in that it reduces the capacityincrease by the power increase needed to power the more complextransceiver. Exploitation of this multipath approach requires both highpower (to permit data transport over the relatively weaker additionaldiversity path) and complex codecs (to permit data transports at highrates on the dominant path by filtering out the diversity pathtransmissions). To the extent that the nodes differ in their antennaemix, this approach complicates the administration and management of thenetwork by constraining the potential path exploitation topreviously-known or approved channels where the required equipment foreach diverse path is known to exist.

Spatially distributed networks overcome this particular limitation byexploiting the inherent diversity between internode channel responses inthe network. This diversity exists regardless of any multipath presenton any individual path in the network, i.e. it does not require highlevels of opportunistic multipath to be exploitable by the system.Moreover, such spatial diversity can be designed into the network bycareful choice of topologies for the nodes during the deploymentprocess, in order to provide linear growth in capacity as transceiversare added to the network. As a side benefit, the network can spatiallyexcise transmissions from compromised nodes and emitters, allowingsecure, high quality service in environments with external interference.

A downside to such an approach is its obvious weakness to unexpectedgrowth, dynamic changes in topology (from mobile, transitory, ortransient nodes), or dramatic changes in relative channel densities.Unlike the present form of the invention, such an approach does nothandle well unplanned-for competition, environmental changes, or readilyexploit opportunities arising from surprisingly (i.e. unplanned for)good network performance.

MIMO Networks: Shapes and Spaces

The complex MIMO environment and multiple dimensions of differentiation(spatial, frequency, time, code), the physical geography of any network(ring, star, mesh), the physical geographies of the surrounding terrain(creating the multipaths) and the other wireless signals from outsidethe network, and the internal network environment (of traffic patternsand node differences) create a diversity explosion.

To create and manage optimal network capacity, the preferred embodimentcreates a network topology that enforces a constraint where each nodewith M(n) antennae has ≦M(n) other nodes in its view with whom itcommunicates at any particular interval of time. This may take the formof a ring (see FIG. 16), star (FIG. 18), mesh (FIGS. 39, 40), orcombination thereof, depending on the individual nodes' hardware andgeographic specifics. Moreover, this may dictate the placement of nodes,geographically or in uplink or downlink transmission subsets. Thisenables the creation of reciprocal subspaces for each sub-set of thenetwork and therefore for the network as an entirety. However, theapproach in the preferred embodiment can manage with other networkshapes and spaces, just as it can manage with the hardware or protocolor software constraints inherent in particular nodes.

While the preferred embodiment works with reciprocal subspaces, whereinthe network maintains reciprocity according to Eq. 1 between nodes overall links joining them, some parameters may be allowed to vary andcreate asymmetric spaces. For example, in a carrier offset case, thechannel responses are actually invariant but for the complex scalarsinusoid which creates the frequency offset; physically, this is anon-reciprocal link but logically it remains (assuming signal contentdensity on both sides is kept equal) a substantively reciprocal link.Other adaptation means are permissible as long as the network designrule of Eq. 1 is kept as a high priority.

One major difference in the present embodiment of the invention fromprior art is that by making the control and feedback aspects part of thesignal encoding process, and thereby eliminating or at least reducingthe need for a separate channel(s) for control and feedback, the networkcontent overhead is reduced and an additional range within the signaldimension is available for signal content. The LEGO reduction to single,or small, bit sized power management signals can be similarly echoed forother network management, depending on the target objective the networkelects.

Furthermore, because the MIMO and LEGO approach described herein isusable in any network topology, and with existing protocols and schema(PSK/QAM; FDD; CDMA, including modulation-on-symbol, or synchronous,CDMA; TDMA; SDMA, etc.) the network can adapt to a diverse environmentof users rather than requiring all to have the identical hardware,software, and standards.

The diversity of transmission and reception at all nodes in the network,rather than just at a subset of hub nodes or BSs, means that every nodecan use in its local environment any redundancy in transmission orreception of data over multiple channels, whether they be spatial,polarization, spectral, temporal, or any combination thereof. Themaximum use can be made of all available (i.e. unused by others)signaling lacunae, with the nodes adaptively adjusting to the trafficand external environmental conditions according to the objectives set bythe network.

Furthermore, the present embodiment of the invention does not require apreliminary calibration of the transceiver array, the communicationschannel, or geographic site as do many approaches used in the prior art.The continuous feedback and rapid convergence of the approach allow forflexibility and adaptivity that will permit correction of miscalibrateddata, when the miscalibration represents a no-longer valid model of theenvironment for the receiving node.

Additionally, the MIMO network of the present embodiment is adaptive tochannel response changes due to network point failures. This includes:the ability to survive element failure at individual nodes (i.e. oneantenna, or transceiver, fails) without loss of communication to thatnode (though it may incur possible loss of capacity); the ability tosurvive failure of links without loss of communication to that node(e.g., by routing data through other paths); the ability to survivefailure of node (all links terminating at that node) without undulyaffecting connectivity or capacity of network; and the ability toachieve network reliability that is higher than reliability of any nodein that network. The network will automatically adjust itself to optimalperformance in event of any of these failures; potentially by reroutingactive links based on available SINR experienced at that link.

Application Areas and Advantages: MIMO

The incorporation of the control and feedback signal as part of theprocess rather than as discrete, separate, and particular parts of thesignal, can decrease the complexity of apparatus by removing the needfor a separate channel for network control. It also can decrease thecomplexity of the processing by removing the need for particulardedication of a time aspect of the reception, or by removing the needfor additional detection and interpretation of control signal from othercontent through either software or hardware. Moreover, suchincorporation also integrates the entire aspect of power management andcontrol into the signaling process rather than artificially andneedlessly separating it from the network dynamics. This integrationallows both capacity and power control to cooperatively handle packetacknowledgment, signal synchronization, and transmit/receivefunctionality at each node, and to optimize their conjoinedfunctionality to the needs of the environment, the user, or the noderather than being constrained to disparate, pre-set and non-dynamicdictates by network administration that are only responsive to the realworld environment to the extent that the system designers' assumptionsaccurately modeled the real-world and unknowable complexities.

Because the control and feedback signaling is incorporated into theprocess, the present form of the invention does not impose overheadconstraints or capacity demands upon the network to nearly the samedegree as the prior art does. For a given infrastructure andenvironment, therefore, the present form of the invention providesincreased capacity and performance through dynamic, and self-moderatingsignal processing algorithms, with a minimal overhead.

Additionally, because the method does not require a strict hierarchicaldivision between Base Station and Subscriber Unit nodes, but ratheradapts to the diversity of reception and transmission at each particularnode depending on its then-current environmental context, the methodallows for rapid and responsive deployment of mixed hardware units beingconditioned by factors external to the network, such as user choice oreconomic limitations.

Unlike prior art, the present form of the invention will work with eachof CDMA, FDD, TDMA, SDMA, and PSK/QAM implementations, and with anycombination thereof. Because the present form of the invention will workwith diverse environments, where the diversity may come from within thenetwork (rather than from sources external to it), this protects usersand companies' investments in prior infrastructure and avoids creatingeither a ‘captive service market’ subject to crippling and suddeninnovation, or creating a network which will suffer when aChristiansen-style disruptive technology advance arrives. Moreover,diversity reception, which is the redundant transmission and receptionof data over multiple channels (whether the diversity comes in spatial,polarization, spectral, or temporal form, or any combination thereof),permits successful operation in environmental conditions which wouldotherwise block any particular channel or perfect subset of channels.This means that the present form of the invention will continue tooperate in dirty, bursty, or difficult conditions, whether the impact ofthe negative force is on the nodes or the external environment.

As such, there are a number of potential implementations whichimmediately become feasible for a dynamically adaptive network, in themilitary and security fields. These include military and civilianapplications where individual unit or node failure can be anticipatedand therefore must not bring down the network, and where environmentalconditions can become disruptive for particular nodes or links. Thesewould also include support and exploration applications where theexternal environment (including node location) and network internalenvironment (traffic, connectivity) may change over time, as thecomponent nodes move and change capabilities and capacities.

Among the effects which enhance the ready establishment and dynamic useof security advantages through the present form of the invention are thethree-layer pilot signal (network mask plus originator mask plusrecipient mask) detailed below (See FIG. 21). This allows users tocommunicate both on an unsecured overall network and a separately securesub-network, on discrete (possibly encrypted) subnets through a subnetmask. This also allows network establishment and alteration of anysubnet through designation and adaptation of shared subnet masks,wherein layers of encryption become algorithmically establishable. Thepresent form of the invention also allows the fast detection,acquisition, interference excision, and reception of originatorsattempting to talk with the recipient, prioritizing the same accordingto their match to any set of subnet masks (highly secured signalspresumably taking priority over less secured or open signals).Alternative uses of origination masks or recipient masks allowdual-natured communication priorities and the ability to suppressunintended recipients via the imposition of either origination orrecipient masks, the secure transmission through interim nodes notprovided with either mask, and the ability to determine and remove groupdelay as a fundamental part of the FLS algorithm.

Unlike the prior art, the present form of the invention will also allowfor optional specialization (e.g. in transmission, reception,flow-through channelization) at any particular node in a dynamicfashion, thereby allowing the network as a whole to adapt to transientenvironmental fluctuations without concomitant alteration inon-the-ground hardware or in-the-system software alterations. Such readyadaptivity increases the total cost-effectiveness, as well as thedynamic stability for the entire network.

Unlike prior art, the present form of the invention supports diversityreception and transmission at all nodes in the network. This creates alevel of flexibility, adaptivity to environmental or network changes,and dynamic stability which increases the ready scalability overmultiple distinct approaches simultaneously or serially accepted by thenetwork. Since the core reciprocity and protocols can be used bydistinctly different hardware and signals, the present form of theinvention permits local accretive growth rather than demanding top-down,network-wide initial standardization, thereby decreasing the capital andplanning cost for implementing or changing a network.

Another advantage of the present form of the invention is that itpermits shared antenna usage amongst multiple nodes, thereby decreasingthe number of antenna necessary for any given node to attain aparticular capacity, and thereby decreasing for the network as a wholethe cost and complexity required for that same level of capacity.Furthermore, it also permits any set of nodes to use a diversity ofchannels without requiring an increase in the antenna or internalcomplexity (in both hardware and software) at every node in said set ofnodes.

A further advantage of the present form of the invention is the abilityto adaptively select and use ad-hoc, single-frequency networks on all orpart of the network, under conditions when network traffic is ‘bursty’,that is, when there are significant disparities between the high and lowcontent volumes of traffic being communicated amongst that part of thenetwork.

A particularly significant advantage of the present form of theinvention is that using the reciprocity equation equalizes theprocessing or duty cycle for message transmission and reception acrossboth directions of a link, thereby lowering the processing imbalancewhich otherwise might be created between transmission and receptionmodes. This in turn reduces the average complexity which must be builtinto each particular node by decreasing the maximal capacity it must becreated to handle for an overall network minimal capacity average.

Another advantage of the present form of the invention is that, unlikemuch of the prior art, the present form of the invention will work inuncalibrated areas where the environmental context is either previouslyunknown or altered from previous conditions. This allows for rapid,uniphase adoption and expansion in any given area without requiringprior to the adoption the precise calculation of all environmentaleffects upon transmissions and receptions within said area at allplanned or possible node locations. This further allows the adoption anduse for transient, or mobile, nodes in areas without requiring allpossible combinations of channel responses amongst said nodes firstbeing calibrated and then said channel responses matched to currentconditions, or constrained to pre-set limitations.

Another advantage of the present form of the invention is that itprovides rapid correction for miscalibrated data, thereby reducing thecost of inaccurate measurement, human or other measurement error, orincorrect calibration calculations. This in turn reduces the overheadand planning required for adaptation for any given network to aparticular environment, either initially or as the environment changesover time, as the channel responses in the real world can be readilyadapted to.

A concomitant advantage of the present form of the invention is therapid and dynamic adaptation to channel response changes when a networkfailure, at any particular node or sub-set of nodes, occurs. Thisgreatly increases the stability and durability of any networkincorporating the present form of the invention without the level ofcost, complexity, or duplication required by the present state of theart. Amongst the advantages conveyed are the ability for the network tosurvive partial failure at any particular node without being forced todrop or lose that node (i.e. maintaining maximal attainable capacitybetween that node and all others to which is can communicate), theability to survive the total lose of any particular node, by sharing thesignal traffic amongst alternative channels. The present form of theinvention also permits the rerouting of active links around ‘lost’ or‘damaged’ nodes without human intervention by adherence to the newreciprocity measurements. And the increase in network stability toexceed not just the reliability of each particular node, but the averagereliability of all nodes for, while any subset of nodes still remainsoperable, the maximal network capacity for that set can be maintained.This is unlike the present state of the art, where if 50% of the nodesof a network fail then the average network communication drops to zero,as all the channels lose one half of their pairs. Furthermore, thepresent form of the invention enables the network to automaticallyadjust itself and its optimal performance in the event of any partialfailure without requiring human intervention, thereby decreasing thecost and increasing the responsiveness of the network. Even moreimportant is the fact that, upon incremental re-instatement orrestoration of particular node or channel function, network optimizationcontinually advances without manual re-establishment.

Another advantage of the present form of the invention is that itminimizes the complexity, and increases the accuracy, of the signalweight update operation at each particular node and for the network as awhole.

Another advantage of the present form of the invention is that itprovides a computationally efficient mechanization of cross-correlationoperations for both nodes and channels across and within the network. Asthe number of signals simultaneously processed on a singletime-frequency channel grows, the marginal complexity increase caused byaddition of those signals drops, for fast adaptation methods such asautocorrelation approaches, e.g. inverse-based or least-squares). Thisis because the Digital Signal Processing (DSP) cycles needed forhigh-complexity operations in fast techniques, such as matrixcomputations, QR decompositions, or data whitening operations common toDSP processing, can be amortized over the larger number of signals. Thehigher overhead of hardware and software complexity needed to handlesignal complexity thereby is lowered on a per-signal basis the greaterthe complexity actually used by the system. For certain techniques suchas pilot-based or least-squares signal weighting, the fast techniquesbecome less complex than the current conventional approaches such asLeast-Mean-Squares or stochastic gradient, wherein the overhead remainsindifferent to the increasing complexity of the signals being processed.When the number of signals that must be processed is equal to one-halfthe number of combiner weights used at an adaptive receiver, forexample, then the crossover in overhead complexity vs. speed occursbetween least squares and least mean squares.

Because the present form of the invention is dynamically adaptive, itcan use any subordinate portion (in time, channels, or network subset)of the process wherein a ‘reciprocal subspace’ exists to implement itsfull value. Even though the parameters of the signal processing may varybetween the uplink (transmission) and downlink (reception) phasesbetween any two nodes on any given channel or link, to the extent thatthey overlap such a reciprocal subspace can be effectuated and used. Forexample, a reciprocal subspace can be created where there is a carrieroffset, where the channel responses are distinguished solely by a scalarcomplex sinusoid (e.g. a frequency offset), between the two nodes,regardless of which is, at any particular moment, transmitting orreceiving.

Since the present form of the invention with its non-orthogonalmultitone capability allows the addition of mobile, transient, ortemporary nodes to any network, it creates a system that can manage andprovision any combination of fixed, portable, low mobility, and highmobility nodes and links. The capacity constraints being node andchannel specific rather than network delimited also permits theheterogeneous combination of differing capacities, thereby allowingperipheral distinctiveness, creating the potential for a system with ahierarchy of nodes including high-function, base-function, andlimited-function or even single-function (e.g. appliance) nodes.

The present form of the invention permits the ability to achieve nearlylinear increases in capability, even superlinear increases (in dirtyenvironments) for increases in infrastructure, namely the RF transceivercapacities within the network. This is a significant advance over theprior state of the art for PTP networks which achieve sublinear capacitygrowth with network infrastructure growth.

In non-fully loaded networks using the present form of the invention,the MIMO connectivity can provide sharply higher data rates toindividual channels or nodes where the additional information flow, tothe maximal capacity of the particular nodes, is routed through nodeswhich have intended reception or transmission capacity available. Thisis a ‘water balancing’ approach to traffic maximization available onlywhen multiple rather than single path capabilities are establishedthrough a network, or any network structure that instantiates fixedbottlenecks (e.g. ‘star’ or ‘hub’ topologies, BS PMP networks, orfixed-channel PTP networks).

Additionally, the reciprocity approach enables an automatic and dynamicload sharing amongst the channels and nodes which minimizes bottlenecksor, in the military or security environment, desirable targets ofopportunity for ‘hot centers’ of traffic. Commercially this is morevaluable by reducing the power and complexity requirements of what inPTP and PMP networks are BSs to attain a given network capacity andpower efficiency.

The preferred embodiment of the present form of the invention includes anumber of interacting and synergistic elements, both in hardware and inoperational software. The preferred embodiment, as a network, willincorporate particular functional elements at individual nodes, as wellas overall systemic features which may not be shared by or incorporatedin the hardware of each particular node (i.e. there may existspecialization amongst the nodes). As stated in the summary, each nodepreferentially has an antennae array; multiple, multitone, transceivers(one per antenna); and constrains itself to reciprocal uplinks anddownlinks (FIGS. 13 A and 13B). The antennae array is spatially and/orpolarization diverse and transmits and receives signal energy duringalternating time slots (or sequences of time slots in TDD-TDMA systems).Each transceiver is a vector OFDM transceiver, with digital signalprocessing elements, that downconverts, A/D converts, and frequencychannelizes data induced on each antenna (or other diversity channel)during receive time slots, and inverse channelizes, D/A converts, andupconverts data intended for each antenna (or diversity channel) duringtransmit time slots; linearly combines data received over each diversitychannel, on each frequency channel and receive time slot; redundantlydistributes data intended for each diversity channel, on each frequencychannel and transmit time slots; and computes combiner and distributorweights that exploit the, narrowband, MIMO channels response on eachfrequency channel and time slot (FIG. 15). Although the preferredembodiment of the invention allows individual nodes to vary greatly intheir capacities, a set of nodes preferentially will incorporate thehardware capabilities detailed in the following paragraphs.

The first preference is that the transmission element be a multi-tonefront end, using OFDM with cyclic prefixes at fixed terminals(generally, BS) (FIG. 22, Items 314, 318, 322,) and generalizedmultitone with guard-time gaps (FIG. 22, Item 310) at mobile terminals(generally, SU). To minimize aperture blur, the system uses tonegrouping into narrowband frequency channels (FIG. 22, Item 348). TheOFDM can be readily implemented in hardware using Fixed-FourierTransform enabling chips; it also simplified the equalization procedure,eliminated decision feedback, and provides a synergistic blend withadaptive arrays. An alternative uses frequency-channelized PSK/QAM withmodulation-on-symbol CDMA (that is, synchronous CDMA).

Each node of the network incorporates a MIMO transceiver. FIG. 24displays a functional representation of such, and the hardware andprocessing is detailed over the next several paragraphs.

Each MIMO transceiver possesses an antennae array where the antennae arespatially separated and the antennae array itself is preferentiallycircularly symmetric (FIG. 15, Item 110). This provides 1-to-M modes (RFfeeds) for the signals to be transmitted or received over, maximizes theseparability of transmission links, enables a scalable DSP backend, andrenders the MIMO transceiver fault-tolerant to LNA failures.

In an alternative embodiment the transceiver sends the transmissionsignal through Butler Mode Forming circuitry (FIG. 25, Item 380), whichincludes in a further embodiment a Band Pass Filter (FIG. 25. Item 382)where the transmission is reciprocally formed with the shared Receiverfeeds, and the number of modes out equals the numbers of antennae,established as an ordered set with decreasing energy. The Butler ModeForming circuitry also provides the spatial signal separationadaptation, preferentially with a FFT-LS algorithm that integrates thelink separation operation with the pilot/data sorting, link detection,multilink combination, and equalizer weight calculation operations. ThisButler Mode Forming approach means that the transmission forming isreadily reciprocal with the receiver feeds (also shared), makes thetransmission fault tolerant for PA (Phase-Amplitude) failures, andenables a readily scalable DSP front end; it also enables thetransceiver to ratchet the number of antennae used for a particulartransmission or reception up or down.

Having passed through the Butler Mode Forming circuitry, thetransmission is then sent through the transmission switch (FIG. 15, Item112), with the uplink frequencies being processed by the LNA bank(FIG.15, Item 113), moderated by an AGC(FIG. 15, Item 114), and the downlinkfrequencies being processed by a PA bank(FIG. 15, Item 124). The LNAbank also instantiates the low noise characteristics for the outgoingsignal and communicates the characteristics to the PA bank to properlymanage the power amplification of the incoming signals to moderate thetransmission overlap.

Further transmission switch processing then hands off the transmissionto the frequency translator (FIG. 15, Item 115), which is itselfgoverned in part by the Los circuit(FIG. 15, Item 116). The transmissionswitch throughout is controlled by a controller (FIG. 15, Item 120) suchthat basebank link distribution of the outgoing signals takes place suchthat energy is distributed over the multiple RF feeds on each channel,steering up to K_(feed) beams and nulls independently on each FDMAchannel in order to enhance node and network capacity and coverage. Thiscontrol further greatly reduces the link fade margin and that node's PArequirements.

From the transmission switch the transmission goes to an ADC bank(FIG.15, Item 117), while a received signal will come from a DAC bank (FIG.15, Item 123), the complexity of the analog/digital/analog conversiondetermining the circuit mix within the banks.

Then from the ADC bank the transmission flows through a MultitoneDemodulator Bank (FIG. 15, Item 118), which splits it into 1 through KFDMA channels, where K is the number of feeds. The now separated tones(1 through M for each channel) in aggregate forms the entire basebandfor the transmission, which combines spatial, polarization, either, orboth, feeds across the FDMA channels or even combines up to K FDMAchannels as transmission data density requires. This combination enablessteering a greater number of beams and nulls than the RF feeds, up tothe number of feeds times the number of FDMA channels. It also separatesup to K_(feed) links per FDMA channel, improves overall transmissionlink error and/or retransmission rates, improves overall networkcapacity and coverage, and reduces the link fade margins, reduces the PAcost, and reduces battery consumption at the other ends of the link.

From the Multitone Demodulator Bank the Rx data is passed to circuitryfor mapping the received broadband multitone signal into separated,narrowband frequency channels and time slots (FIG. 15, Item 119).

An outgoing transmission signal experiences the reverse of the aboveprocess; having been mapped to tones and RF feeds (FIG. 15, Item 121),it passes into a Multitone Modulator bank (FIG. 15, Item 122), an DACbank (FIG. 15 Item 123), the transceiver switch, the FrequencyTranslator, the transceiver switch, the PA bank element (FIG. 15, Item124), the transceiver switch, and thence in the preferred embodimentthrough the Butler Mode Form and on to the RF T/R feeds (FIG. 24, Item130) and to the antennae array and the particular transmission antennaetherein (FIG. 15, Item 125)

The transmission switch throughout is controlled such that baseband linkdistribution of the outgoing signals takes place such that energy isdistributed over the multiple RF feeds on each channel, steering up toK_(feed) beams and nulls independently on each FDMA channel in order toenhance node and network capacity and coverage. This control furthergreatly reduces the link fade margin and that node's PA requirements.

The particular Multitone MOD and DEMOD elements (FIG. 15, Item 118 and119) in a node vary according to whether it will be handling Fixed,Portable, Low-Mobility, and/or High-Mobility Nodes. Generally, a signalpassing into the MT DEMOD may be passed through a comb filter, where a128-bit sample is run through a 2:1 comb; then passed through an FFTelement, preferably with a 1,024 real-IF function; and then mapped tothe data using 426 active receive ‘bins’. Each bin covers a bandwidth of5.75 MHz with an inner 4.26 MHz passband, so each of the 426 bins has 10MHz. The middle frequency, bin 256, will be at 2.56 MHz, leaving abuffer of 745 kHz on either side of the content envelope. Within thetransmission, when it passes through the MT MOD, presuming each link is100 μs, 12.5 μs at each end of the transmission is added as a cyclicprefix buffer and cyclic suffix buffer, to allow for timing error. (FIG.22, Items 314, 3181, 322.) In an alternative embodiment, presuming thatonly a cyclic prefix is needed, the system can either double the size ofthe prefix (FIG. 22, Item 310) or add the suffix to the signal time. Thereverse processing as appropriate (i.e. stripping off the cyclic prefixand suffix buffers) is not shown but is well known to the art.

The 426 bins form 13 channels and 426 tones, (FIG. 26, Item 430), witheach Channel forming 320 kHz and 32 tons (FIG. 26, Item 432), beingfurther organized with an upper and lower guard tone (FIG. 26, Items 438and 436, respectively) and 30 information bearing tones (FIG. 26, Item440). An alternative embodiment for a high-mobility environment halvesthe numbers of tones and doubles the MHz, so there are only 213 bins(and tones) for 4.26 MHz (FIG. 27, Item 442), and each channel onlycarries 16 tones (FIG. 27, Item 446), with 1 being an guard tone (FIG.27, Item 448) and fifteen being information-carrying tones (FIG. 27,Item 450).

For non-fixed embodiments, the timing modifications may be varied. Thesignal being processed is handed first to a MUX where anelement-multiply with a Tx or Rx (for transmit or receive, respectively)window is performed. The guard time is retained to serve as dead timebetween signals, effectively punctuating them. The high-mobilityembodiment halves the number of bins, doubling the average bin size, anduses duplication to increase QoS within the multitone. (FIG. 26.)

The next stage through the MIMO transceiver is the incorporation (on thetransmission side) or interpretation (on the reception side) of theQAM/PSK symbols, prior to the signal's passing through the MIMOtransceiver exits (if being transmitted) or enters (if being received)through a Link codec. Each FDMA channel will separate through the codecinto 1 through M links, and each Link codec will incorporate toneequalization and ITI remove as necessary. The Link codec also includesSOVA bit recovery, error coding and error detection, and packagefragment retransmission methodologies.

An optional alternative embodiment would at this point further includetone/slot interleaving (for the reception) or deleaving (for thetransmission). A further optional alternative embodiment would replacethe TCM codec and SOVA decoder with a Turbo codec.

Another optional alternative will incorporate dual-polarization. (SeeFIG. 25.) Fundamentally, this halves the modes and complexity of thetransmissions and receptions, while doubling the capacity for anyparticular link/PA power constraint. In this alternative embodiment, theantennae array provides 1-to-(M/2) modes (RF feeds) for downconversionand demodularization. The Butler Mode Forming splits the modes intopositive and negative polarities, where the negative polarization hasthe opposite, and normally orthogonal, polarization to the positivepath. Preferentially the Butler Mode Forming works with circularpolarizations. This alternative embodiment enables scalable DSPtransmission and reception paths and renders the entirety fault tolerantto LNA/PA failures. At the last stage (for transmission; the firststage, for reception) the signal passes through a dual-polarized Linkcodec. That links the nodes over the dual polarizations, doubles thecapacity under any particular link/PA power constraint, greatly reducesthe codec complexity (and thus cost), and the link/PA power requirementfor any particular link rate constant.

The Transceiver DSP backend for the preferred embodiment is detailed inFIG. 15. The Butler Mode Forming element with its RF transmission andreception leads, is controlled by the T/R switch control, which in turnis subject to the system clock and synchronization subsystem. Antransmission signal (which can be continuous, periodic, triggered,human-determined, reactive, context-sensitive, data quality or quantitysensitive) that forms a Tx link message passes through a symbol encoderbank and into the circuitry where Delay/ITI/pilot gating are imposed,said circuitry being linked to its reciprocal for received signals. Thetransmission data symbols, over k channels, now pass through themultilink diversity distribution circuitry, where for each channel ktransmission gains G(k) are adapted to the proper weighting, asdetermined by the multilink, LEGO gain adaptation element (with bothalgorithms and circuitry). From the multilink diversity distribution thetransmission next is mapped over diversity modes and FFT bins, thenhanded to the transmission/reception compensation bank Here, accordingto the perceived environment of transmissions and reception and theparticular Transmission/Reception compensation algorithm used, thetransmission is passed to the inverse frequency channel bank and,finally, into the Butler Mode Forming element. This Transceiver DSPbackend also passes the information about the transmission signal fromthe compensation bank element to the synchronization subsystem.

The LEGO gain adaptation element at each node enables the network tooptimally balance the power use against capacity for each channel, link,and node, and hence for the network as a whole. FIG. 32 discloses thefundamental form of the algorithm used.

A capacity objective β for a particular node 2 receiving from anothernode 1 is set as the target to be achieved by node 2. Node 2 solves theconstrained local optimization problem:

$\begin{matrix}{{\min\limits_{\pi_{1}{(q)}}{\sum\limits_{q}\; {\pi_{1}(q)}}} = {1^{T}\pi_{1}\mspace{14mu} {such}\mspace{14mu} {that}}} & {{EQ}.\mspace{14mu} 3}\end{matrix}$Σ_(q∈Q(m))log(1+γ(q))≧β(m),  EQ. 4

where π₁ (q) is the SU (user 1 node) transmit power for link number q,

γ(q) is the signal to interference noise ratio (SINR) seen at the outputof the beamformer,

is a vector of all 1s,

and

π₁ is a vector whose q^(th) element is p₁(q).

The aggregate set Q(m) contains a set of links that are grouped togetherfor the purpose of measuring capacity flows through those links.

An example of this would be if SU had connections to multiple BSs, andwe were primarily concerned with the total information flow into and outof a given node. In this case all of the links that connected to thatnode would be in the same aggregate set. Also in this description, wehave adopted the convention that each transmit path from a transmitterto a receiver for a given narrow-band frequency channel is given aseparate link number, even if the BS and SU are the same. Thus multipletransmit modes, that say exploit multipath or polarization diversity,are each given different link numbers, even though the source anddestination nodes might be identical. Moreover, if a BS/SU pair transmitover multiple frequency channels, then each channel is given a separatelink number. (This simplifies notation considerably.)

An example of this is shown in FIG. 19. The BSs are represented bycircles and the SUs by triangles. Each arrow represents a communicationlink. The BSs and SUs can be dynamically combined into proper subsets oftransmit uplink and receive uplink. The choice of aggregate sets can bearbitrary, provided no link is in multiple aggregate sets. However in apreferred embodiment, the aggregate sets are links that share a commonnode and hence common, readily available channel parameters.

The downlink objective function can be written as:

min Σ_(q)π₂(q)=1^(T)π₂ such that  EQ. 5

Σ_(q∈Q(m))log(1+γ(q))≧β(m)  EQ. 6

The required feasibility condition, that Σ_(q∈Q(m)) π₁(q)≦R₁(m) isreported to the network, and in the preferred embodiment, reported to anetwork controller, so that β(m) can be modified as needed to staywithin the constraints.

In an alternative embodiment, the capacity constraints β(m) aredetermined in advance for each aggregate set, based on known QoSrequirements for given nodes or group of nodes. The objective functionthen seeks to minimize the total power in the network as suggested byEQ. 4.

By defining the noise normalized power transfer matrix by:

P _(rt)(q,j)=|w ^(H) _(r)(q)H _(rt)(q,j)g _(t)(j)|²,  EQ. 7

where W_(r)(q) is a receiver weight vector for link q, and,g_(t)(j) is the transmit weight vector for link.By unit normalizing the receive and transmit weights with respect to thebackground interference autocorrelation matrix, the local model canstate:

w _(r) ^(H)(q)R _(i) _(r) _(,i) _(r) (q)w _(r)(q)=1, and g _(t) ^(T)(q)R_(i) _(r) _(,i) _(r) (q)g _(t)*(q)=1  EQ 52

enabling the nodal model to express the SINR equation as:

$\begin{matrix}{{\gamma (q)} = \frac{{P_{rt}\left( {q,q} \right)}{\pi_{t}(q)}}{1 + {\sum\limits_{j \neq q}\; {{P_{rt}\left( {q,j} \right)}{\pi_{t}(j)}}}}} & {{EQ}.\mspace{14mu} 8}\end{matrix}$

Accordingly, a matrix condition can be defined on the range of possibleoutput SINRs; and from this, π_(t) has a feasible, that is non-negativesolution, if and only if:

ρ(δ(γ)(P _(rt)−δ(P _(rt))<1,  EQ. 9

where ρ(M) is the spectral radius of a matrix M,the non-negative power transfer matrix P_(rt) has qj'th element given inEQ. 7,δ(γ) is a diagonal matrix whose q′th element is γ(q)

and

δ(P_(rt)) is a diagonal matrix with the same diagonal as P_(rt).The weight normalization in EQ. 52, and the assumption of reciprocalchannel matrices leads to the reciprocity equation (EQ. 1), and the factthat the uplink and downlink objective functions in EQ. 3 and EQ. 4 areidentical for the same target SINRs.

Various means for solving the optimization in EQ. 3 exist; the preferredembodiment uses a very simple approximation for γ(q), as very weakconstraints to the transmit powers are all that are needed to yieldobjective functions which satisfy the reciprocity equation (EQ. 2).

Another approach can take advantage of the case where all the aggregatesets contains a single link, and we have non-negligible environmentalnoise or interference. For smaller networks, all the channel transfergains in the matrices P₁₂ and P₂₁ are estimated and the transmit powersare computed as Perron vectors from:

$\begin{matrix}\begin{matrix}{D_{21} = {\log \left( {1 + \frac{1}{{p\left( p_{21} \right)} - 1}} \right)}} \\{= {\log \left( {1 + \frac{1}{{p\left( p_{12}^{T} \right)} - 1}} \right)}} \\{= D_{12}}\end{matrix} & {{EQ}.\mspace{14mu} 10}\end{matrix}$

In this case a simple power constraint is imposed upon the transmitpowers, so that they remain feasible. The optimization is alternatingdirections, first the weights are optimized, then the powers areobtained from the Perron vectors, and the process is repeated.

Another embodiment assumes effectively that the denominator in Eq. 8remains approximately constant even after changes to the power levels inother nodes in the network (hence the local optimization approach),because the beamformer weights in the network (transmit and receive) inthe MIMO approach will attempt to cancel the co-channel interference inthe network, making it insensitive to power level changes of theinterferers. The denominator in Eq. 8 represents the post beamforminginterference seen by the receiver associated with link q for the forwardlink (downlink) if r=1, and the reverse link if r=2.

With this approximation, and a rewriting of Eq. 8 (for the uplink) to:

$\begin{matrix}{{\gamma (q)} \approx \frac{{P_{21}\left( {q,q} \right)}{\pi_{1}(q)}}{i_{2}(q)}} & {{EQ}.\mspace{14mu} 11}\end{matrix}$

where

i ₂(q)=1+Σ_(j≠q) P ₂₁(q,j)π₁(j)  EQ 12

is the post beamforming interference energy, and is assumed constant forthe adjustment interval for current transmit power values, the node cansolve EQ. 3 in closed form using classic water filling arguments basedon Lagrange multipliers. A similar equation is established for thedownlink.

An alternative embodiment of Eq. 11 is to measure, provide, and useactual information for additional, available, or important terms in thedenominator of Eq. 8 and to incorporate them into Eq. 12, and thenrather than closed form use successive applications thereof to themodified problem using local data.

Another alternative embodiment is to solve, at each node, theconstrained optimization problem:

$\begin{matrix}{{\max\limits_{m}{\sum\limits_{q \in {Q{(m)}}}\; {\log \left( {1 + {\gamma (q)}} \right)}}},{{such}\mspace{14mu} {that}}} & {{EQ}.\mspace{14mu} 13} \\{{{\sum\limits_{q \in {Q{(m)}}}{\pi_{1}(q)}} \leq {R_{1}(m)}},{{\gamma (q)} \geq 0}} & {{EQ}.\mspace{14mu} 14}\end{matrix}$

using the approximation in Eq. 11, which is a water-filling solutionsimilar to that described above for Eq. 3. This solution requires ahigh-level network optimizer to control the power constraints, R₁(q), todrive the network to a max-min solution.

The preferred embodiment, however, solves the local problem byattempting to minimize the total power as a function of the targetoutput SINR. The output SINR will be the ratio of square of the channeltransfer gain times the transmit power, divided by the interferencepower seen at the output of the beamformer, where:

γ(q)=|h ²(q)|π₁(q)/i ₂(q)  EQ. 15

γ(q)=|h ²(q)|π₂(q)/i ₁(q)  EQ. 16

where

|h(q)² is the square of the channel transfer gain,

π₁(q) is the transmit power for link q during the reverse link or uplinktransmission,

π₂(q) is the transmit power for link q during forward link or downlinktransmission,

i₁(q) is the interference power seen at the output of the beamformerused by the SU associated with link q,

-   -   and,

i₂(q) is the interference power seen at the output of the beamformerused by the BD associated with link q.

This makes the output SINR a function of all the transmit powers at allthe other SUs in the network. Additionally, by normalizing thebeamforming weights with respect to the background interference, it ispossible to maintain the reciprocity equation even in the presence ofarbitrary interference and noise, due to non-cooperative signal sources,such as jammers or co-channel communication devices. Maximizing the SINRyields optimal receiver weights that can remove the effect of jammersand co-channel interferers. The reciprocity equation insures that theoptimal transmit weights puts substantive nulls in the direction ofthese same co-channel interferers. For military applications, thisimplies that the network reduces it's probability of detection andinterception, and for co-channel communication systems, it reduces it'stransmitted interference, and is effectively a ‘good neighbor’permitting system deployment in otherwise unacceptable environments.Commercially, this allows a network employing the present embodiment ofthis invention to cope with competitive, impinging, wireless networknodes and transmissions.

It can be shown that there is a 1-1 mapping between all the transmitpowers and all of the output SINRs, i.e. there exists a vector valuedfunction F₁ such that F₁(γ)=π₁.

The function has an inverse so that F₁ ⁻¹(π₁)=γ. A key result that isexploited by this embodiment is the fact that if the channels arereciprocal, then the objective functions, and the constraint set imposedby (1) is identical as a function of γ for both the uplink and downlinkobjective functions. Mathematically this means these objective functionscan be stated in general terms as:

f(γ)=1^(T) F ₁(γ)=1^(T) F ₂(γ),  EQ. 17

where π₂=F₂(γ) is the mapping between the SINRs and the BS transmitpowers.

In the preferred embodiment, each node uses the above as it defines andgenerates its local model as follows:

Given an initial γ₀ generate the model,

L(γ,g,β)=g ^(T)γ  EQ. 20

Σ_(q∈Q(m))log(1+γ(q))≧β(m)  EQ. 21

g=∇ _(γ) f(γ₀)  EQ. 22

where

L(γ,g,β) is a linearized model of the objective function,

g^(T)γ is an inner product between the gradient of the objectivefunction and a set of target SINRs,

Σ_(q∈Q(m))log(1+γ(q))≧β(m). is the capacity constraint for aggregate setm,

-   -   and,

g=∇_(γ)f(γ₀) is the gradient of the objective function (the totaltransmit power) as a function of the target SINRs.

The new γ_(α) is updated from

γ*=arg min_(γ) L(γ,g,β)  EQ. 23

γ_(α)=γ₀+α(γ*−γ₀)  EQ. 24

The constant α is chosen between 0 and 1 and dampens the update step ofthe algorithm. This determines a target SINR that the algorithm adaptsto. The update for the transmit power for link q becomes,

π₂(q)=γ_(α) i ₁(q)/h(q)²  EQ. 25

π₁(q)=γ_(α) i ₂(q)//h(q)²  EQ. 26

Where i₁(q) and i₂(q) are the post beamforming interference power seenat the SU and the BS respectively for link q.

The present embodiment of this invention uses advantageously the factthat the q^(th) element of the gradient of the objective function can bewritten as the product of the interference powers divided by the squareof the transfer gain:

{∇_(γ) f(γ₀)}_(q) =i ₁(q)i ₂(q)//|h(q)|².  EQ. 27

The transmit power update relationship in Eq. 25 and Eq. 26 can beapplied repeatedly for a fixed feasible γ_(α) and the convergence ofπ₁→F₁(γ_(α)) is guaranteed. In fact some assert this convergence will beguaranteed if we optimize the receive weights at each iteration. (SeeVisotsky, E; Madhow, U, “Optimum Beamforming Using Transmit AntennaArrays”, Vehicular Technology Conference, 1999 IEEE 4^(th), Volume 1, pp851-856, though he only considered the effects in a Rank 1 channel, thatis a single narrowband rather than a MIMO channel.) A similar statementholds for π₂→F₂(γ_(α)). In an alternative embodiment where the properrelationship is unknown, or dynamically changing, then a suitably longblock of N samples is used to establish the relationship, where N iseither 4 times the number of antennae or 128, whichever is larger, withthe result being used to update the receive weights at each end of thelink, optimize the local model in Eq. 23 and Eq. 24, and then apply Eq.25 and Eq. 26.

The algorithm used in the preferred embodiment enables the network, andlocal nodes thereof, to attain several important results. First, foreach aggregate set m, the optimization of the local model(s) at eachnode(s) completely decouples the network optimization problem to anindependent (set) of local problem(s) that is solved among the aggregateset links. Accordingly, within a given aggregate set, we inherit thenetwork objective function model:

L_(m)(γ,g,β)≡Σ_(q∈Q(m))g_(q)γ(q)  EQ. 28

Σ_(q∈Q(m))log(1+γ(q))≧β(m)  EQ. 29

g _(q) =i ₁(q)i ₂(q)//|h(q)|²  EQ. 30

where

L_(m) (γ,g,β) is the sum of the separable, linearized, objectivefunctions corresponding to the aggregate set number m, where eachlocalized objective function depends only on variables that pertain tothe given aggregate set,

g_(q)γ(q) is the product of the q′th element of the gradient vector withthe SINR for link q,

g_(q) is the q′th element of the gradient vector,

and,

|h(q)|² is the square of the channel transfer gain from the transmitbeamformer, through the channel to the output beamformer (not includingthe transmit power).

Second, this approach eliminates matrix channel estimation as anecessary step, as solving the local problem only requires that anestimate of the post beamforming interference power, a single realnumber for each link, be transmitted to the other end of the link, or inanother embodiment to the node assigned to computing the transmit powersfor a given aggregate set. For each link, a single real number, thetransmit power, is then propagated back to the transmitter. This is trueeven for networks with large rank MIMO channel matrices.

Third, the optimization problem, which is stated in general terms in Eq.17, when you plug in a formula for π as a function of γ into theobjective function, i.e. 1^(T)F_(r)(γ) for the SINR to power mappingF_(r)(γ), is reduced from a complex and potentially unsolvable problemto one that has a simple closed form solution, and thus can use a wellknown water filling problem seen in classical information theory (see T.Cover, T. Joy, Elements of Information Theory, Wiley; 1991); MatthewBromberg and Brian Agee, “The LEGO approach for achieving max-mincapacity in reciprocal multipoint networks,” in Proceedings of theThirty Fourth Asilomar Conf. Signals, Systems, and Computers, October2000.

Fourth, even when starting from non-feasible starting points, thealgorithm rapidly converges; in the preferred embodiment, where allparameters are updated after every receive block, it converges to afixed point within the vicinity of the optimal solution; and in analternative embodiment, where the γ_(α) vector is held fixed untilπ₁→F₁(γ_(α)) and π₂→F₂(γ_(α)) before updating the weights and updatingγ_(α) again.

A figure illustrating the computational tasks at the BS and the SU for agiven link q is shown in FIG. 34. It is assumed that the BS is assignedthe task of computing the transmit gains for this particular example.The figure shows that only two numbers are transferred from the BS tothe SU and from the SU to the BS. The basic computational tasks at eachnode are also shown.

In the preferred embodiment, only one side of the link need perform thepower management computations. One of the principle advantages andstrengths of the present embodiment of the invention is that it replaceshalf of the prior art's explicit, dual computations with an implicitcomputation that is performed by the physical transmission of data,which generates the real-world interference (and thus interferencevalues) used by the power control algorithm.

The estimation of the transfer gains and the post beamforminginterference power is done efficiently in the preferred embodiment withsimple least squares estimation techniques.

The problem of estimating the transfer gains and the post beamforminginterference power (in the preferred embodiment, by using a leastsquares algorithm) is equivalent to solving for the transfer gain h asfollows:

y(n)=hgs(n)+ε(n)  EQ. 31

where y(n) is the output of the beamformer at the time sample,h≈w^(H) _(r)(q)H₂₁(q,q)g_(t)(q), whose square modulus is P_(rt)(q,q),

w_(r)(q) is the receive weight vector for link q,

g_(t)(q) is transmit weight vector for link q,

g is the square root of the transmit power π_(t)(q),s(n) is the transmitted complex symbol at time sample n,

and

ε(n) represents all of the remaining co-channel interference and noise.(Indexing is dropped to avoid clutter.) Then y(n) is defined as theoutput after applying the unit normalized despread weights to thereceived data. This is simply the usual beamformer output divided by thenorm of the despread weights with respect to the noise covariancematrix; and for many applications, this will be a scaled multiple of theidentity matrix.

Using a block of N samples of data, h is then estimated as:

$\begin{matrix}{h = \frac{\sum\limits_{n = 1}^{N}\; {(n){\gamma (n)}}}{\sum\limits_{n = 1}^{N}{{{s(n)}}^{2}g}}} & {{EQ}.\mspace{14mu} 32}\end{matrix}$

where h is the channel transfer gain,s*(n) is the conjugate of s(n),y(n) is the output of the beamformer at the time sample n,and,

s(n) is the transmitted complex symbol at time sample n.

From this an estimation of the residual interference power, R_(ε), whichis identified with i₁(q) in Eq. 11 by:

$\begin{matrix}{R_{ɛ} = {{\langle{{ɛ(n)}}^{2}\rangle} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}{\left( {{{y(n)}}^{2} - {{{ghs}(n)}}^{2}} \right).}}}}} & {{EQ}.\mspace{14mu} 33}\end{matrix}$

where

gh is the product of the transmit gain and the post-beamforming channelgain.

The knowledge of the transmitted data symbols s(n) in the preferredembodiment comes from using remodulated symbols at the output of thecodec. Alternative embodiments use the output of a property restoralalgorithm used in a blind beamforming algorithm such as constant modulusor constellation restoral, or by using a training sequence explicitlytransmitted to train beamforming weights and asset the power managementalgorithms, or other means known to the art. This information, and theknowledge of the data transmit power values π₁(q) will be at thereceiver and can be transmitted to the transmitter as part of a datalink layer message; and if the processing occurs over fairly largeblocks of data the transfer consumes only a small portion of theavailable bandwidth. Means for handling the case when a transmit mode isshut off, so that one of the π₁(q)=0, include removing the index (q)from the optimization procedure and making no channel measurements.

In the preferred embodiment, a link level optimizer and decisionalgorithm (See FIGS. 32A and 32B) is incorporated in each node; itsinputs include the target and the bounds for that node, and its outputsinclude the new transmit powers and indications to the network as to howthe node is satisfying the constraints. FIG. 33 indicates a decisionalgorithm used by the link level optimizer.

In an alternative embodiment, the solution to Eq. 3 is implemented byusing a variety of Lagrange multiplier techniques. In other alternativeembodiments, the solution to Eq. 3 is implemented by using a variety ofpenalty function techniques. All of these embody techniques known to theart for solving the local problem. One such alternative takes thederivative of γ(q) with respect to π₁ and uses the Kronecker-Deltafunction and the weighted background noise; in separate alternativeembodiments, the noise term can be neglected or normalized to one. Anapproximation uses the receive weights, particularly when null-steeringefforts are being made, and as the optimal solution will have weightsthat approach the singular vectors of the interference-whitened MIMOchannel response. In the situation where the links of a given aggregateset Q(m) are all connected to a single node in the network, allinformation pertaining to the subchannels and propagation modes of theMIMO channel associated with that node are available, hence the normsquared transfer gain P₂₁(q,q) is available for all q∈Q(m) from theprocessing used to obtain the MMSE receive beamforming weights.

In the preferred embodiment, adaptation of the power is done in a seriesof measured and quantized descent steps and ascent steps, to minimizethe amount of control bits that need to be supported by the network.However, in an alternative embodiment, a node may use more bits ofcontrol information to signal for and quantize large steps.

Various alternative methods can be used to develop the local model foreach node. The preferred embodiment's use of measured data (e.g.function values or gradients) to develop the local model valid invicinity of the current parameter values, is only one approach; it can,however, be readily optimized within the node and network. The usualmodel of choice in prior art has been the quadratic model, but this wasinadequate as elements of the functions are monotic. One alternativeembodiment is to use a log-linear fractional model:

$\begin{matrix}{{\beta_{q} \approx {\log \left( \frac{{a\; {\pi_{1}(q)}} + a_{0}}{{b\; {\pi_{1}(q)}} + b_{0}} \right)}} = {{\hat{\beta}}_{q}\left( {\pi_{1}(q)} \right)}} & {{EQ}.\mspace{14mu} 34}\end{matrix}$

where β_(q) is the achievable bit capacity as a function of the transmitgains π₁(q);and to characterize the inequality

{circumflex over (β)}_(q)(π₁(q))≧β  EQ. 35

with a linear half-subspace, and then solving the approximation problemwith a simple low dimensional linear program.

Another alternative embodiment develops the local mode by matchingfunction values and gradients between the current model and the actualfunction. And another develops the model as a solution to the leastsquares fit, evaluated over several points.

Because of the isolating effect of the transmit and receive weights thefact that the transmit weights for the other nodes in the network maychange mitigates the effect on the local model for each node. Yetanother alternative broadens the objective function to include theeffect of other links in the network, viewing them as responding to someextent to the transmit values of the current link q. A finer embodimentreduces the cross-coupling effect by allowing only a subset of links toupdate at any one particular time, wherein the subset members are chosenas those which are more likely to be isolated from one another.

In the preferred embodiment, and as shown in the figures, Node 2optimizes the receiver weights during the uplink (when sending) using aMMSE function; then measures the SINR over all paths k for a particularchannel q, and informs the sending node 1 both of the measured capacityfor channel q, that is, (D₁₂(q)) and, if the measured capacityexperienced for that channel is too high, to lower the power, or, if themeasured capacity for that channel is too low, to increase the power,with the power increase or decrease being done by small, discreteincrements. Node 2 then sets, for that channel, the transmit weights tothe receive weights and repeats this sub-process for the downlink case.By successive, rapid iteration node 2 rapidly informs node 1 of theprecise transmission power needed at node 1 to communicate over channelq with node 2.

This process is performed for every channel q which is active at node 2,until either the target capacity is attained, or the capacity cannot beimproved further. It is also repeated at every node in the network, sonode 1 will be telling node 2 whether node 2 must increase or decreasethe power for node 2's transmissions to node 1 over channel q.

In an alternative embodiment, the network contains one or more networkcontrollers, each of whom govern a subset of the network. The networkcontroller initiates, monitors, and changes the target objective (in thepreferred alternative embodiment, capacity) for the set of nodes itgoverns and communicates the current objective to those nodes and therest of the network as necessary. (See FIG. 33.) Different sub-networkscan use different capacity objectives depending on each network'slocalized environment (both external and internal, i.e. trafficdensity).

The network controller, once it has initialized the reciprocal set andobjective continually monitors the network of nodes it governs,continually compares if the desired capacity has been reached, and foreach node n, perform a fitting function. (See FIG. LE2) If a node n iscompliant with the power constraints and capacity bound, then R₁(n)should be reduced by a small amount; but if node n has both powerconstraints and capacity violations than R₁(n) should be incremented forthat node. These increments and decrements are preferably quantized tofixed small numbers. In an alternative embodiment of the invention thescalar and history of the increments and decrements are recorded to feedinto experientially modified approximations, effectively embodying areal-world adaptation learning for each node.

One important consequence of this approach is that compliance with anynetwork constraint or objective can be conveyed with a single bit, andincrement or decrement with two bits, thereby reducing the controloverhead to a minimum.

From the Butler Mode Forming element received signals are first passedthrough the frequency channel bank, then mapped to the FDMA channels.The received data on channel k will be passed through both the MultilinkReceive weight adaptation algorithm and the Multilink diversitycombining, Receive weights W(k) element, which in turn both feed intothe Multilink LEGO gain adaptation algorithm and thus feed-back into themultilink diversity distribution element for outgoing transmissions. TheMultilink Receive weight adaptation algorithm passes the adapted datafrom channel k over to the Multilink Diversity combining, ReceiveWeights W(k) element passes on the signal to both the circuits for theEqualization algorithm and the Delay/ITI/pilot-gating removal bank, thatstrips out the channel-coordinating information and passes the nowcombined signal to the symbol decoder bank to be turned into theinformation which had been sent out from the originating transceiver,the inverse process, generally, from the symbol encoding at thetransmission end.

These Signal Encoding Operations are graphically displayed in FIG. 21.(Because the decoding is both the inverse and well enough known, given aparticular encoding, to be within the state of the art for anypractitioner in the field, there is not a for the inverse, SymbolDecoding Operations.) A given signal, such as an IP datagram, is formedinto a fragment and passed along to a MUX element. (Any signal which canbe equated to or converted into an IP datagram, for example an ATM,would either be converted prior to this point or handled similarly.) Thedesired MAC header data, which in one alternative embodiment isoptionally time-stamped, is also fed into the same MUX element where thetwo combine. This combined signal now passes through a CRC generator aswell as feeding into a second MUX that combines the CRC output with it.Next, the signal passes into an encryption element that also performstrellis encoding. (In an alternative embodiment one or both of theseoperations are eliminated, which reduces the transceiver's hardware andsoftware complexity but decreases the network's security andreliability.) (For more information on the alternative use of Trelliscoded modulation, see, Boulle, et al., “An Overview of Trellis CodedModulation Research in COST 231”, IEEE PIMRC '94, pp. 105-109.) Thenow-encoded signal is next passed to the element where it is mapped tothe individual tones and the MT symbols, and where buffer tones and timeand frequency interleaving is done. A second, optional, delaypreemphasis signal element, and a third signal element from a pilotgenerator, taking input from the originating node, recipient node,group, or network, or any combination or sub-combination thereof, noware combined with the signal from the mapping element in a MUX. This MUXmay use the first two slots for a pilot without modulation by theinformation tones, using the remaining slots for the pilot modulated bythe information tones to further harden the pilot/signal combination. Analternative embodiment would at this point further pass the transmissionsignal through an ITI pre-distortion element; otherwise, thenow-encoded, piloted, and mapped transmission signal is ready.

Pilot tone generation, summarily disclosed on FIG. 21, is furtherdetailed in FIG. 28. Information concerning one or more of theoriginating node, recipient node, and network or group channelorganization flow into a pilot signal generator, and the resulting pilotsignal is further modified by a network code mask. This multilayer maskthen is used to form a signal with a pseudorandom sequence that isshared by all nodes in the same network or group, though the sequencemay vary over channels and MT symbols to allow further coordinationamongst them at the receiving end. Passing on the signal is modified inan element-wise multiplication (typically a matrix operation, embeddedin hardware) by a signal that indexes on the originating node, which inan optional variation includes a nodal pseudodelay, unique to that nodein the network or group, which overlay again may vary over channels andframes to improve security. The originating node index overlay is acomplex, exponential phase ramping. The combined signal now mixes with arecipient node index, another pseudorandom sequence that is unique tothe recipient node, modifying the whole in a second element-wisemultiplication. Thus the final pilot tone reflects the content signal,modified to uniquely identify both the originating node and its context,and the receiving node and its context, effecting a signal compositionthat allows the network to pilot the communication through the networkfrom origin to destination regardless of the intervening channels ittakes.

When a communication is transmitted, it will be received; and the MIMOreception is, like the transmission, adaptive. See FIG. 30, detailingthe logical processing involved. Received data passes through both aMultilink weight adaptation algorithm and (to which that part iscombined) a Multilink diversity combining of the Reception weights. Thisreweighted transmission now passes through the equalization algorithmand (to which that is combined) a Delay/ITI/pilot-gating removal bankstage. These sort out the properly weighted tones, perform therecombinations, and undo the pilot-gating distortion to effectivelyreassemble at the reception end the symbol pattern of the originalsignal. That now passes through a symbol decoder bank to recover themessage from the symbolic representation and the whole now is joinedwith the other received and linked messages for final reassembly. Thefunctional and firmware processing (fixed logic hardware, limitedpurpose firmware, or combined software, processors and circuitry). Thereceived symbol X(i,1), comprising a matrix combination of L Link and Mmultitone elements, is first modified by the pilot tone generator thatsends the recipient node, network, and group modifications for anelement-wise reverse multiplication, to strip off that component of thesignal and identify if the received symbol is from any originatingsource trying to send to this particular recipient. If the recipientpilot signal matches, then the signal passed on to a circuit thatseparates the pilot signal elements from the data signal elements. Thepilot signal elements are passed through a link detection circuit thatpreferentially uses a FFT-LS algorithm to produce link qualitystatistics for that particular received transmission, identifies theweighting elements that were contained in the pilot signal and passesthose over to the multilink combination circuit, and sends the pilotweights over to the circuit for equalizing weight calculations. The datasignal, combined with the pilot weighting elements, now is combined withthe equalizer calculated factors to strip off all pilot information fromthe traffic data. Next, the re-refined traffic data passes through alink demodulator to produce the original channel-by-channel link streamsof data. In an alternative embodiment, the first channel, which has beenreserved for decryption, decoding, and error detection signalling, notpasses through the ITI correction circuitry and thence to theinstantiated decryption, decoding, or retransmission circuitry asindicated by the data elements of the first channel signal; meanwhile,the remaining data channel elements are available, having been refinedfrom the combined received elements.

A MIMO transceiver contains and uses simultaneously a multiple of singleRF feeds. A signal passes between the Butler Mode Forming element and aBand Pass Filter, or preselection, element, and then between the BandPass Filter element and the Transceiver switch. If the signal is beingtransmitted, it goes through a Low Noise Amplifier element and then backinto the Transceiver switch; if the signal is being received, it goesthrough a Phase Amplifier and back into the Transceiver switch. Thesignal passes between the Transceiver switch and the Frequencytranslator, and then back into the Transceiver switch.

In the Frequency translator (FIG. 25), the signal passes through asecond Band Pass Filter with a Surface Acoustic Wave (SAW) Frequencygreater than three, then between that second Band Pass Filter and afirst mixer, where it will be mixed (or unmixed, depending on direction)with (or by) another waveform which has come from the timing element(s),which may be any of the system clock, synchronization subsystem, and GPStime transfer, or their combination. The combined timing and contentsignal passes between the first mixer and a SAW element where it iscombined (or separated) with a saw frequency of less than or equal to1.35 times that of the signal. The SAW-modified signal passes betweenthe SAW element and a second mixer, where the saw-modified signal ismixed (or unmixed, depending on direction) by the waveform which alsohas come from the timing element(s) mentioned above. The signal passesbetween the second mixer and the LPF element with a SAW Frequencygreater than three; the next transition is between the frequencytranslator and the Transceiver switch. Depending on the direction of thesignal, it passes between the Transceiver switch and the ADC element orthe DAC element (the ADC and DAC together are ‘the converter elements’)and the Transceiver switch, and between the ADC element and the FFT/IFFTelement or between the FFT/IFFT element and the DAC element. Both theDAC and ADC elements are linked to and governed by the system clock,while the signal's passage through the Transceiver switch and the otherelements (LNA or PA, Frequency Translator, and between the Transceiverswitch and the converter elements, is governed by the Switch Controllerelement. This approach is used because the Frequency Translator can beimplemented as a single piece of hardware which lowers the cost of theoverall unit and lessens the signal correction necessary.

Different multitone formats are used at different transceivers, therebyenabling ready distinction by and amongst the receivers of thetransmitter frequency tone set. For fixed transceivers (BS or fixed SU),rectangular windows with cyclic prefixes and/or buffers are used; formobile transceivers, non-rectangular windows and guard times are used.This provides the network with a capacity fall-back as the networkenvironment and traffic dynamics vary. In the preferred embodiment theguard times are matched to the cyclic prefixes and buffers, themultitone QAM symbols are matched at all windows, and the differentwindows and capacity are used in different modes.

The multitone (multifrequency) transmission that occurs between everypair of nodes when they form a communications link exploits themultipath phenomena to achieve high QoS results. Each node, when it isacting as a receiver, optimizes the receive weights, using the MMSEtechnique. This goes directly against Varanesi's assessment that“de-correlative and even linear MMSE strategies are ill-advised for suchchannels because they either do not exist, and even if they do, they areplagued by large noise-enhancement factors”.

An alternative embodiment uses the Max SINR technique, and anycombination of these and other industry-standard receiver optimizationalgorithms are feasible alternative implementations. Then the transmitweights for that node in its reply are optimized by making themproportional to the receive weights. Finally, the transmit gains (gainmultipliers that multiply the transmit weights) are optimized accordingto a max-min capacity criterion for that node, such as the max-min sumof link capacities for that transceiver node at that particular time. Analternative embodiment includes as part of the network one or morenetwork controllers that assist in tuning the local nodes' maximumcapacity criterion to network constraints, e.g. by enforcing a balancingthat reflects an intermediate nodes' current capacity which is lowerthan the local, originating node's current capacity.

The MIMO network model for the aggregate data transmitted between N₁“Set 1 nodes” {n_(i)(1), . . . , n₁(N₁)},receiving data over downlinktime slots and transmitting data over uplink time slots, and N₂ “Set 2nodes” {n₂(1), . . . , n₂(N₂)} receiving data over uplink time slots andtransmitting data over downlink time slots, can be approximated by

x₂(k,l)≈i₂(k,l)+H₂₁(k,l)s₁(k,l)  EQ. 36

(uplink network channel model)

x₁(k,l)≈i₁(k,l)+H₁₂(k,l)s₂(k,l)  EQ. 37

(downlink network channel model)within frequency-time channel (k,l) (e.g., tone k within OFDM symbol l)transmitted and received at uplink frequency f₂₁(k) and time t₂₁(l) anddownlink frequency f₁₂(k) and time t₁₂(l), where

-   -   s₁(k,l)=[s^(T) ₁(k, l; n₁(1)) . . . s^(T) ₁(k, l; n₁(N₁))]^(T)        represents the network signal vector transmitted from nodes        {n₁(p)} within uplink channel (k,l);    -   s₂(k,l)=[s^(T) ₂(k, l; n₂(1)) . . . s^(T) ₂(k, l; n₂(N₂))]^(T)        represents the network signal vector transmitted from nodes {n₂        (q)} within downlink channel (k,l);    -   x₁(k,l)=[x^(T) ₁(k, l; n₁(1)) . . . x^(T) ₁(k, l; n₁(N₁))]^(T)        represents the network signal vector received at nodes {n₁(p)}        within downlink channel (k,l);    -   x₂(k,l)=[x^(T) ₂(k, l; n₂(1)) . . . x^(T) ₂(k, l; n₂(N₂))]^(T)        represents the network signal vector received at nodes {n₂(q)}        within uplink channel (k,l)_(;)    -   i₁(k,l)=[i^(T) ₁(k, l; n₁(1)) . . . i^(T) ₁(k, l; n₁(N₁))]^(T)        models the network interference vector received at nodes {n₁(p)}        within downlink channel (k,l);    -   i₂(k,l)=[i^(T) ₂(k, l; n₂(1)) . . . 1 ^(T) ₂(k, l; n₂(N₂))]^(T)        models the network interference vector received at nodes {n₂(q)}        within uplink channel (k,l);    -   H₂₁(k,l)=[H₂₁(k, l; n₂(q),n₁((p))] models the channel response        between transmit nodes {n₁(p)} and receive nodes {n₂(q)} within        uplink channel (k,l); and    -   H₁₂(k,l)=[H₁₂(k, l; n₁(p),n₂(q))] models the channel response        between transmit nodes {n₂ (q)} and receive nodes {n₁(p)} within        downlink channel (k,l);        and ( )^(T) denotes the matrix transpose operation, and where    -   s₁(k, l; n₁) represents the M₁(n₁)×1 node n₁ signal vector        transmitted over M₁(n₁) diversity channels (e.g., antenna feeds)        within uplink frequency-time channel (k,l);    -   s₂(k, l; n₂) represents the M₂(n₂)×1 node n₂ signal vector        transmitted over M₂(n₂) diversity channels within downlink        frequency-time channel (k,l);    -   x₁(k, l; n₁) represents the node n₁ signal vector received over        M₁(n₁) diversity channels within downlink frequency-time channel        (k,l);    -   x₂(k, l; n₂) represents the M₂(n₂)×1 node n₂ signal vector        received over M₂(n₂) diversity channels within uplink        frequency-time channel (k,l);    -   i₁ (k, l; n₁) models the M₁(n₁)×1 node n₁ interference vector        received over M₁(n₁) diversity channels within downlink        frequency-time channel (k,l);    -   i₂(k, l; n₂) models the M₂(n₂)×1 node n₂ interference vector        received over M₂(n₂) diversity channels within uplink        frequency-time channel (k,l);    -   H₂₁(k, l; n₂,n₁) models the M₂(n₂)×M₁(n₁) channel response        matrix between transmit node n₁ and receive node n₂ diversity        channels, within uplink channel (k,l); and    -   H₁₂(k, l; n₁, n₂) models the M₁(n₁)×M₂(n₂) channel response        matrix between transmit node n₂ and receive node n₁ diversity        channels, within downlink channel (k,l).

In the absence of far-field multipath between individual nodes, H₂₁(k,l; n₂, n₁) and H₁₂(k, l; n₁, n₂) can be further approximated by rank 1matrices:

H₂₁(k,l;n₂,n₁)≈λ₂₁(n₁,n₂)a₂(f₂₁(k),t₂₁(l);n₁,n₂)a^(T)₁(f₂₁(k),t₂₁(l);n₂,n₁)×exp{−j2π(τ₂₁(n₂,n₁)f₂₁(k)−v₂₁(n₂,n₁)t₁₂(l))}]  EQ.38

H₁₂(k,l;n₁,n₂)≈λ₁₂(n₂,n₁)a₁(f₁₂(k),t₁₂(l);n₂,n₁)a^(T)₂(f₁₂(k),t₁₂(l);n₁,n₂)×exp{−j2π(τ₁₂(n₁,n₂)f₁₂(k)−v₁₂(n₁,n₂)t₁₂(l))}  EQ.39

where

-   -   λ₂₁(n₂, n₁) models the observed uplink pathloss and phase shift        between transmit node n₁ and receive node n₂;    -   λ₁₂(n₁, n₂) models the observed downlink pathloss and phase        shift between transmit node n₂ and receive node n₁;    -   τ₂₁ (n₂, n₁) models the observed uplink timing offset (delay)        between transmit node n₁ and receive node n₂;

τ₁₂ (n₁, n₂) models the observed downlink timing offset between transmitnode n₂ and receive node n₁;

v₂₁ (n₂, n₁) models the observed uplink carrier offset between transmitnode n₁ and receive node n₂;

-   -   v₁₂(n₁, n₂) models the observed downlink carrier offset between        transmit node n₂ and receive node n₁;    -   a₁(f,t; n₂, n₁) models the M₁(n₁)×1 node n₁ channel response        vector, between node n₂ and each diversity channel used at node        n₁, at frequency f and time t; and    -   a₂(f,t; n₁, n₂) models the M₂(n₂)×1 node n₂ channel response        vector, between node n₁ and each diversity channel used at node        n₂, at frequency f and time t.

In many applications, for example, many airborne and satellitecommunication networks, channel response vector a₁ (f,t; n₂, n₁) can becharacterized by the observed (possibly time-varying) azimuth andelevation {θ₁(t; n₂, n₁), φ₁(f,t; n₂, n₁)} of node n₂ observed at n₁. Inother applications, for example, many terrestrial communication systems,a₁(f,t; n₂, n₁) can be characterized as a superposition of direct-pathand near-field reflection path channel responses, e.g., due toscatterers in the vicinity of n₁, such that each element of a₁(f,t; n₂,n₁) can be modeled as a random process, possibly varying over time andfrequency. Similar properties hold for a₂(f,t; n₁, n₂).

In either case, a₁(f,t; n₂, n₁) and a₁(f,t; n₁, n₂) can be substantivelyfrequency invariant over significant breadths of frequency, e.g.,bandwidths commensurate with frequency channelization used in 2G and 2.5G communication systems. Similarly, a₁(f,t; n₂, n₁) and a₂(f,t; n₁, n₂)can be substantively time invariant over significant time durations,e.g., large numbers of OFDM symbols or TDMA time frames. In these cases,the most significant frequency and time variation is induced by theobserved timing and carrier offset on each link.

In many networks of practical interest, e.g., TDD networks, the transmitand receive frequencies are identical (f₂₁(k)=f₁₂(k)=f(k)) and thetransmit and receive time slots are separated by short time intervals(t₂₁(l)=t₁₂((l)+Δ₂₁≈t(l)), and H₂₁(k,l) and H₂₁(k,l) becomesubstantively reciprocal, such that the subarrays comprising H₂₁(k,l)and H₂₁(k,l) satisfy H₂₁(k, l; n₂, n₁)≈δ₂₁(k, l; n₁, n₂) H^(T) ₁₂(k, l;n₁, n₂), where δ₂₁(k, l; n₁, n₂) is a unit-magnitude, generallynonreciprocal scalar.

If the observed timing offsets, carrier offsets, and phase offsets arealso equalized, such that λ₂₁(n₂, n₁)≈λ₁₂(n₁, n₂), τ₂₁(n₂, n₁)≈τ₁₂(n₁,n₂), and v₂₁(n₁, n₂)≈v₁₂(n₂, n₁), for example, by synchronizing eachnode to an external, universal time and frequency standard such asGlobal Position System Universal Time Coordinates (GPS UTC), then δ₂₁(k,l; n₁, n₂)=1 can be obtained and the network channel response becomestruly reciprocal, H₂₁(k,l)≈H^(T) ₁₂(k,l). However, this more stringentlevel of reciprocity is not required to obtain the primary benefit ofthe invention.

In order to obtain substantive reciprocity, each node in the networkmust possess means for compensating local differences between transmitand reception paths. Methods for accomplishing this using probe antennasare described in Agee, et. al. (U.S. patent application Ser. No.08/804,619, referenced above). A noteworthy advantage of this inventionis that substantive reciprocity can be obtained using only localtransmit/receive compensation means.

The channel model described above is extendable to applications wherethe internode channel responses possess substantive multipath, such thatH₂₁(k, l; n₂, n₁) and H₁₂(k, l; n₂, n₁) have rank greater than unity.This channel response can also be made substantively reciprocal, suchthat the primary benefit of the invention can be obtained here.

The preferred embodiment uses a substantively null-steering networkwherein each node transmits baseband data (complex symbols provided by amultirate codec) through the multiplicity of reciprocal linear matrixoperations prior to transmission into the antenna array during transmitoperations, and after reception by the antenna array during receiveoperations, in a manner that physically separates messages intended forseparate recipients. This is accomplished by

(1) forming uplink and downlink transmit signals using the matrixformula

s ₁(k,l;n ₁)=G ₁(k,l;n ₁)d ₁(k,l;n ₁)

s ₂(k,l;n ₁)=G ₂(k,l;n ₂)d ₂(k,l;n ₂)  EQ. 40

where

d ₁(k,l;n ₁)=[d ₁(k,l;n ₂(1),n ₁) . . . d ₁(k,l;n ₂(N ₂),n ₁)]^(T)

-   -   represents the vector of complex data symbols transmitted from        node n_(i) and intended for each of nodes {n₂(q)}, respectively,        within uplink channel (k, l);

d ₂(k,l;n ₂)=[d ₂(k,l;n ₁(1),n ₂) . . . d ₂(k,l;n ₁(N ₁),n ₂)]^(T)

-   -   represents the vector of complex data symbols transmitted from        node n₂ and intended for each of nodes {n₁(q)}, respectively,        within downlink channel (k, l);

G ₁(k,l;n ₁)=[g ₁(k,l;n ₂(1),n ₁) . . . g ₁(k,l;n ₂(N ₂),n ₁)]

-   -   represents the complex distribution weights used to redundantly        distribute symbol vector d₁(k, l; n₁) onto each diversity        channel employed at node n₁ within uplink channel (k, l); and

G ₂(k,l;n ₂)=[g ₂(k,l;n ₁(1),n ₂) . . . g ₂(k,l;n ₁(N ₁),n ₂)]

-   -   represents the complex distribution weights used to redundantly        distribute symbol vector d₂(k, l; n₂) onto each diversity        channel employed at node n₂ within downlink channel (k, l);        (2) reconstructing the data intended for each receive node using        the matrix formula

y ₁(k,l;n ₁)=W ^(H) ₁(k,l;n ₁)x ₁(k,l;n ₁)

y ₂(k,l;n ₂)=W ^(H) ₂(k,l;n ₂)x ₂(k,l;n ₂)  EQ. 41

where ( )^(H) denotes the conjugate-transpose (Hermitian transpose)operation, and where

y ₁(k,l;n ₁)=[y ₁(k,l;n ₂(1),n ₁) . . . y ₁(k,l;n ₂(N ₂),n ₁)]^(T)

-   -   represents the vector of complex data symbols intended for node        n₁ and transmitted from each of nodes {n₂ (q)}, respectively,        within downlink channel (k, l);

y ₂(k,l;n ₂)=[y ₂(k,l;n ₁(1),n ₂) . . . y ₂(k,l;n ₁(N ₁),n ₂)]^(T)

-   -   represents the vector of complex data symbols intended for node        n₂ and transmitted from each of nodes {n₁(p)}, respectively,        within uplink channel (k, l);

W ₁(k,l;n ₁)=[w ₁(k,l;n ₂(1),n ₁) . . . w ₁(k,l;n ₂(N ₂),n ₁)]

-   -   represents the complex combiner weights used at node n_(i) to        recover symbol symbols {d₁(k, l; n₂(q), n₁)} intended for node        n₁ and transmitted from nodes {n₂(q)} within uplink channel (k,        l); and

W ₂(k,l;n ₂)=[w ₂(k,l;n ₁(1),n ₂) . . . w ₂(k,l;n ₁(N ₁),n ₂)]

-   -   represents the complex combiner weights used at node n₂ to        recover symbol symbols {d₂(k, l; n₁(p), n₂)} intended for node        n₂ and transmitted from nodes {n₁(p)} within uplink channel (k,        l).        (3) developing combiner weights that {w₁(k, l; n₂, n₁)} and        {w₂(k, l; n₁, n₂)} that substantively null data intended for        recipients during the symbol recovery operation, such that for        n₁≠n₂:

|w ^(H) ₁(k,l;n ₂ ,n ₁)a ₁(f ₁₂(k),t ₁₂(l);n ₂ ,n ₁)|<<|w ^(H) ₁(k,l;n ₁,n ₁)a ₁(f ₁₂(k)t ₁₂(l);n ₁ ,n ₁)|  EQ. 42

and

|w ^(H) ₂(k,l;n ₁ ,n ₂)a ₂(f ₂₁(k),t ₂₁(l);n ₁ ,n ₂)|<<|w ^(H) ₂(k,l;n ₂,n ₂)a ₂(f ₂₁(k),t ₂₁(l);n ₂ ,n ₂)|  EQ. 43

(4) developing distribution weights {g₁(k, l; n₂, n₁)} and {g₂(k, l; n₁,n₂)} that perform equivalent substantive nulling operations duringtransmit signal formation operations;(5) scaling distribution weights to optimize network capacity and/orpower criteria, as appropriate for the specific node topology andapplication addressed by the network;(6) removing residual timing and carrier offset remaining after recoveryof the intended network data symbols; and(7) encoding data onto symbol vectors based on the end-to-end SINRobtainable between each transmit and intended recipient node, anddecoding that data after symbol recovery operations, using channelcoding and decoding methods develop in prior art.

In the preferred embodiment, OFDM modulation formats is used toinstantiate the invention, and substantively similar distribution andcombining weights are computed and applied over as broad a range oftones (frequency channels k) and OFDM symbols (time slots l) as ispractical. The range of practical use is determined by the frequencyselectivity (delay spread) and time selectivity (Doppler spread) of thecommunications channel, which determines the degree of invariance of thechannel response vectors a₁ and a₂ on (k,l); the dynamics ofinterference i₁ and i₂; latency requirements of the communicationsnetwork; and dimensionality of linear combiners used at each node in thenetwork, which determine the number of frequency-time channels needed todetermine reliable substantively null-steering distribution andcombining weights.

In the preferred embodiment, substantively nulling combiner weights areformed using an FFT-based least-squares algorithms that adapt {w₁(k, l;n₂, n₁)} and {w₂(k, l; n₁, n₂)} to values that minimize the mean-squareerror (MSE) between the combiner output data and a known segment oftransmitted pilot data. Operations used to implement this techniqueduring receive and transmit operations are shown in FIGS. 35 and 36,respectively. The preferred pilot data is applied to an entire OFDMsymbol at the start of an adaptation frame comprising a single OFDMsymbol containing pilot data followed by a stream of OFDM symbolscontaining information data. The pilot data transmitted over the pilotsymbol is preferably given by

$\begin{matrix}\begin{matrix}{{p_{1}\left( {{k;n_{2}},n_{1}} \right)} = {d_{1}\left( {k,{1;n_{2}},n_{1}} \right)}} \\{= {{p_{01}(k)}{p_{21}\left( {k;n_{2}} \right)}{p_{11}\left( {k;n_{1}} \right)}}}\end{matrix} & {{EQ}.\mspace{14mu} 44} \\\begin{matrix}{{p_{2}\left( {{k;n_{1}},n_{2}} \right)} = {d_{2}\left( {k,{1;n_{1}},n_{2}} \right)}} \\{= {{p_{02}(k)}{p_{12}\left( {k;n_{1}} \right)}{p_{22}\left( {k;n_{2}} \right)}}}\end{matrix} & {{EQ}.\mspace{14mu} 45}\end{matrix}$

where symbol index l is referenced to the start of the adaptation frame,and where

-   -   p₀₁(k) is a pseudorandom, constant modulus uplink “network” or        “subnet” pilot that is known and used at each node in a network        or subnet;    -   p₀₂(k) is a pseudorandom, constant modulus downlink “network” or        “subnet” pilot that known and used at each node in the network;    -   p₂₁(k; n₂) is a pseudorandom, constant modulus uplink        “recipient” pilot that is known and used by every node intending        to transmit data to node n₂ during uplink transmission        intervals;    -   p₁₂(k; n₁) is a pseudorandom, constant modulus downlink        “recipient” pilot that is known and used by every node intending        to transmit data to node n_(i) during downlink transmission        intervals;    -   p₁₁(k; n₁)=exp{j2πδ₁(n₁)k} is a sinusoidal uplink “originator”        pilot that is used by node n₁ during uplink transmission        intervals;    -   p₂₂(k; n₂)=exp{j2πδ₂(n₂)k} is a sinusoidal downlink “originator”        pilot that is used by node n₂ during downlink transmission        intervals;

The “pseudodelays” δ₁(n₁) and δ₂(n₂) can be unique to each transmit node(in small networks), or provisioned at the beginning of communicationwith any given recipient node (in which case each will be a function ofn₁ and n₂). In either case, the minimum spacing between any pseudodelaysused to communicate with a given recipient node should be larger thanthe maximum expected timing offset observed at that recipient node. Thisspacing should also be an integer multiple of 1/K, where K is the numberof tones used in a single FFT-based LS algorithm. If K is not largeenough to provide a sufficiency of pseudodelays, additional OFDM symbolscan be used for transmission of pilot symbols, either lengthening theeffective value of K, or reducing the maximum number of originatingnodes transmitting pilot symbols over the same OFDM symbol (for example,the recipient node can direct 4 originators to transmit their pilotsymbols over the first OFDM symbol in each adaptation frame, and 4 otheroriginators to transmit their pilot symbols over the next OFDM symbol,allowing the recipient node to construct combiner weights for 8originators). In the preferred embodiment, K should also be large enoughto allow effective combiner weights to be constructed from the pilotsymbols alone. The remaining information-bearing symbols in theadaptation frame are then given by

d ₁(k,l;n ₂ ,n _(i))=p ₁(k;n ₂ ,n ₁)d ₀₁(k,l;n ₂ ,n ₁)  EQ. 46

d ₂(k,l;n ₁ ,n ₂)=p ₂(k;n ₁ ,n ₂)d ₀₂(k,l;n ₁ ,n ₂)  EQ. 47

where d₀₁(k, l; n₂, n₁) and d₀₂(k, l; n₁, n₂) are the uplink anddownlink data symbols provided by prior encoding, encryption, symbolrandomization, and channel preemphasis stages.

Preferably, the adaptation frame is tied to the TDD frame, such that theTDD frame comprises an integer number of adaptation frames transmittedin one link direction, followed by an integer number of adaptationframes transmitted in the reverse link direction. However, the OFDMsymbols in the adaptation frame may be interleaved to some degree or anydegree. The pilot data may also be allowed to pseudorandomly varybetween adaptation frames, providing an additional layer of “physicallayer” encryption in secure communication networks.

At the recipient node, the pseudorandom pilot components are firstremoved from the received data by multiplying each tone and symbol bythe pseudorandom components of the pilot signals

x ₀₁(k,l;n ₁)=c ₀₂(k;n ₁)x ₁(k,l;n ₁)  EQ. 53

x ₀₂(k,l;n ₂)=c ₀₁(k;n ₂)x ₂(k,l;n ₂)  EQ. 48

where c₀₂(k; n₁)=[p₀₂(k)p₁₂(k; n₁)]* and c₀₁(k; n₂)=[p₀₁(k)p₂₁(k; n₂)]*are the derandomizing code sequences.

This operation transforms each pilot symbol authorized and intended forthe recipient node into a complex sinusoid with a slope proportional tothe sum of the pseudodelay used during the pilot generation procedure,and the actual observed timing offset for that link (observedpseudodelay). (See FIGS. 21, 28.) Unauthorized pilot symbols, andsymbols intended for other nodes in the network, are not so transformedand continue to appear as random noise at the recipient node (See FIG.38A, 38B).

The FFT-based LS algorithm is shown in FIG. 37. The pilot symbol,notionally denoted X ₀(k,1) in this Figure (i.e., with reference touplink/downlink set and node index suppressed), is multiplied by aunit-norm FFT window function, and passed to a QR decompositionalgorithm, preferably a block modified-Gram-Schmidt Orthogonalization(MGSO), and used to compute orthogonalized data {q(k)} andupper-triangular Cholesky statistics matrix R. Each vector element of{q(k)} is then multiplied by the same window function, and passedthrough a zero-padded inverse Fast Fourier Transform (IFFT) with outputlength PK, with padding factor P, preferably P=4, to formuninterpolated, spatially whitened processor weights {u(m)}, where lagindex m is proportional to target pseudodelay δ(m)=m/PK. The whitenedprocessor weights are then used to estimate the mean-square-error (MSE)obtaining for a signal received at each target pseudodelay,ε(m)=1−∥u(m)∥², yielding a detection statistic (pseudodelay indicatorfunction) with a minimum (valley) at IFFT lags commensurate with theobserved pseudodelay (alternately, combiner output SINR γ(m)=ε⁻¹(m)−1can be measured at each target pseudodelay, yielding a detectionstatistic (peak) at FFT lags commensurate with that pseudodelay. Thepilot symbol, notionally denoted X₀(k,1) in this Figure (i.e., withreference to uplink/downlink set and node index suppressed), ismultiplied by a unit-norm FFT window function, and passed to a QRdecomposition algorithm, preferably a block modified-Gram-SchmidtOrthogonalization (MGSO), and used to compute orthogonalized data {q(k)}and upper-triangular Cholesky statistics matrix R. Each vector elementof {q(k)} is then multiplied by the same window function, and passedthrough a zero-padded inverse Fast Fourier Transform (IFFT) with outputlength PK, with padding factor P, preferably P=4, to formuninterpolated, spatially whitened processor weights {u(m)}, where lagindex m is proportional to target pseudodelay δ(m)=m/PK. The whitenedprocessor weights are then used to estimate the mean-square-error (MSE)obtaining for a signal received at each target pseudodelay,ε(m)=1−∥u(m)∥², yielding a detection statistic (pseudodelay indicatorfunction) with a minimum (valley) at IFFT lags commensurate with theobserved pseudodelay (alternately, combiner output SINR γ(m)=ε⁻¹(m)−1can be measured at each target pseudodelay. The IFFT windowing functionis dependent on the minimum spacing between pseudodelays, and isdesigned to minimize interlag interference (“picket fence” effect)between pilot signal features in the pseudodelay indicator function. Inthe preferred embodiment, and for a node capable of forming four links,a Kaiser-Bessel window with parameter 3 is preferred.

A valley (or peak) finding algorithm is then used to detect each ofthese valleys (or peaks), estimate the location of the observedpseudodelays to sub-lag accuracy, and determine additional ancillarystatistics such as combiner output SINR, input SINR, etc., that areuseful to subsequent processing steps (e.g., LEGO). Depending on thesystem application, either the Q lowest valleys (highest peaks), or allvalleys below a designated MSE threshold (peaks above a designated SINRthreshold) are selected, and spatially whitened weights U areinterpolated from weights near the valleys (peaks). The whitenedcombiner weights U are then used to calculate both unwhitened combinerweights W=R⁻¹U, used in subsequent data recovery operations, and toestimate the received channel aperture matrix A=R^(H)U, to facilitateancillary signal quality measurements and fast network entry in futureadaptation frames. Lastly, the estimated and optimized pseudodelayvector δ* is used to generate c₁(k)=exp{−j2πδ*k} (conjugate of {p₁₁(k;n₁)} during uplink receive operations, and {p₂₂(k; n₂)} during downlinkreceive operations), which is then used to remove the residual observedpseudodelay from the information bearing symbols. (See FIG. 38A, Items702A, 704, 702B, 706, and FIG. 38B, Item 710, for illustration of theoverall signal and the signal modified by the correct origination,target, and pilot mask.)

In an alternate embodiment, the pseudodelay estimation is refined usinga Gauss-Newton recursion using the approximation

exp{−j2πΔ(k−k₀)/PK}≈1−j2πΔ(k−k₀)/PK

This algorithm first estimates Δ, providing an initial sublag estimateof pseudodelay, before estimating the lag position to further accuracy.The resultant algorithm can reduce the padding factor P, and reducesinterpolation errors in the receive combination weights. However, itrequires estimation of an additional IFFT using a modified FFT window,and is therefore not preferred in applications where DSP complexity isof overriding importance.

The optimized combiner weights are substantively null-steering, in thatthe combiner weights associated with each originating signal will(notionally, in absence of multipath) form a composite antenna patternthat steers nulls in the direction of all other time-and-frequencycoincident signals (signals transmitting on the same time slot andfrequency channel) impinging on the array. However, the weights willalso (notionally, in absence of multipath) form a beam in the directionof the originating signal, further improving performance of the overallnetwork. In the presence of multipath, a clear gain pattern of this sortmay not necessarily form; however, the effect of this processing will bethe same, and is typically be improved due the added diversity providedby multipath.

In additional alternate embodiments, the combiner weights can be furtherrefined by exploiting known or added structure of the informationbearing symbols using blind property-restoral algorithms. Algorithms ofthis sort are described in Agee (U.S. Pat. No. 6,118,276) and Agee, et.al., (U.S. patent application Ser. No. 08/804,619, referenced above) aswell as other disclosures in the public domain. These alternateembodiments can reduce the size of K, or allow the airlink to beextended into more complex systems where the linear combinerdimensionalities are too large to allow computation of effective weightsgiven the value of K employed in an existing system.

The resultant network has several useful attributes over prior art. Itis computationally efficient, especially for nodes receiving data fromlarge numbers of originating nodes, since the complex operationsemployed in the FFT-LS algorithm can be amortized over multiple links.It is also rapidly convergent, allowing computation of 4-elementdiversity combiner weights to attain nearly the maximum SINR obtainableby the combiner using 8-to-16 pilot data tones. It automatically detectsand reconstructs data from nodes that have been authorized tocommunicate with the network, or with recipient nodes within thenetwork, and rejects nodes that are not so authorized, allowing thenetwork to adjust and control its topology and information flow at thephysical layer, and providing an important level of security byrejecting signals that do not possess appropriate network or recipientpilots. It also provides an additional level of data scrambling toprevent occurrence of correlated interlink symbol streams that can causesevere misadjustment in conventional linear combiner adaptationalgorithms.

In reciprocal channels, the linear combiner weights provided duringreceive operations can be used to simply construct linear distributionweights during subsequent transmit operations, by setting distributionweight g₁(k, l; n₂, n₁) proportional to w*₁(k, l; n₂, n₁) during uplinktransmit operations, and g₂(k, l; n₁, n₂) proportional to w*₂(k, l; n₁,n₂) during downlink transmit operations. The transmit weights will besubstantively nulling in this system, allowing each node to formfrequency and time coincident two-way links to every node in its fieldof view, with which it is authorized (through establishment of link setand transfer of network/recipient node information) to communicate.

Among other advantages, this capability allows nodes to independentlyadjust transmit power directed to other nodes in the network, forexample, to optimize capacity achievable at that node given the totalpower available to it, or to minimize power emitted into the network bythat node given an aggregate power requirement. This capability alsoallows the node to adjust its contribution to the overall network, forexample, to maximize the total aggregate (max-sum) capacity of thenetwork, or to minimize network power subject to a network-levelcapacity constraint. In addition, this capability can allow the node toprovide two-way communication to authorized nodes, or in definedsubnets, in the presence of other nodes or subnets that it is notauthorized to communicate with, for example, adjacent cells in CMRSnetworks, and adjacent (even interpenetrating), and virtual privatenets. In wireless LAN's and MAN's.

This capability is illustrated in FIGS. 39 and 40, the latter being fora hexagonal network of six nodes arranged in a ring network, with anadditional direct connect between nodes A and D. In this example, eachnode has been provided with a recipient pilot for its adjacent node,e.g., node B has been provided with recipient pilots for nodes A and C,facilitating time-frequency coincident communication with those adjacentnodes. In addition, Node A has been provided with recipient pilots fornode D, i.e., node A can communicate with nodes B, D, or F.

The pseudodelay indicator functions (provided as a function of SINR,i.e., with peaks at observed pseudodelays) are shown for each of thenodes. Indicator functions generated at nodes B, C, E, and F have twostrong peaks, corresponding to pseudodelays used at their connectingnodes, plus a 10 microsecond time-of-flight delay (assuming all nodesare synchronized to GPS UTC). In addition, nodes A and D have a thirdpeak at their respective delays, plus a 20 microsecond time-of-flightdelay. The pseudodelays are minimally separated by 25 microseconds foreach of the originating nodes (12.5 microsecond minimal separationbetween all nodes), which is easily wide enough to allow peaks fromdifferent originators to be discerned. In addition, the peak values(near 20 dB SINR for all links except the A-to-D link) are detectablewith a 0 dB threshold. As Figure ## shows, the receive and transmitweights form beam and null patterns that allow independent links to beformed between authorized nodes, and that allow unauthorized signals tobe screened at the points or reception and transmission.

The aperture estimates A will (also notionally, in absence of multipath)form beams in the direction of the originating node; however; they willignore all other nodes in the network. For this reason, they cannot beused in general to sustain independent links. However, the apertureestimates can be used to allow rapid reentry into the network, forexample, in packet data systems where users may quickly begin and endsignal transmissions over brief time periods (e.g., such that thechannel response has not changed substantively between transmissions).The aperture estimates can also be combined with combiner/distributionweights to form rapid nulls again other links or nodes in the network.

The primary application area for the fully adaptive MIMO arrays of thepreferred embodiment will be below 10 GHz, where the abilities toachieve non-LOS and to exploit multipath are still possible, and wherepathloss, weather effects, and channel dynamics can be handled byadaptive arrays. The preferred embodiment's MIMO network will provide astrong advantage over conventional MIMO links, by not requiring antennaseparation of 10 wavelengths to provide effective capacity gain. Thepresent state of the art considers 10 wavelengths to be the rule ofthumb for the distance between antennas that provides spatiallyindependent antenna feeds due to disparate multipath at each antenna.This rule has greatest applicability in worst-case mobile environmentssubject to Rayleigh fading, i.e., where the (typically much stronger)direct path is obscured, and propagation occurs over many equal-powerreflection paths.

The MIMO network of the preferred embodiment, however, exploits routediversity due to reception of signals from widely separated nodes, anddoes not need multipath to provide the capacity gains cited for MIMOlinks in the present state of the art. This enables the preferredembodiment to employ antennas with much smaller separation, e.g.,circular arrays with half-to-full wavelength diameter, to provideeffective capacity or QoS gains. The preferred embodiment's exploitationof multipath can further improve performance, by providing additionaldifferences between gain and phase induced at each antenna in the array.In this regards, a smaller aperture is also better, as it reducesfrequency selectivity across individual frequency channels. Polarizationdiversity can also be employed between antennas with arbitrary spacing(e.g., in “zero aperture” arrays), as well as “gain diversity” if theantennas have distinct gain patterns.

The MIMO network of the preferred embodiment has application in the10-100 GHz region (for example, LMDS bands around 25-35 GHz where meshnetworks are of increasing interest), even though these networks arelikely to employ nonadaptive directional antennas, or partially adaptiveantennas, e.g., arrays in focal plane of directional dish antennas, thatdirect high gain or “pencil” beams at other ends of the link, ratherthan fully adaptive beam-and-null steering networks. This is due tosmall form factor of such antennas, as well as pathloss, atmosphericabsorption, and weather effects prevalent above the 10 GHz band.

The next preference is that for each channel that is dynamicallyestablished, the uplink and downlink share a common frequency (that is,the transmission and reception are on the same frequency). This enablesthe establishment and exploitation of channel reciprocity (CR) betweenpairs of nodes, the sharing of antennae and diversity channels intransmit and receive operations in particular nodes, and other networkadvantages. The network advantages include the use of ad hoc,single-frequency networks in bursty (data-intensive) networks, such aspacket-switched networks, random-access networks, or at the network“edges” (where the SINR level threatens to overcome the networkcapacity). This also allows the establishment of a two-layertime-division duplex schema in persistent networks or channels (e.g.ones that are circuit-switched, or perform connectionless datagrambackhaul functions), where there is an equal duty cycle in bothdirections. An alternative embodiment will permit asymmetric dutycycles, and yet a third configurable balancing of duty cycles.Additionally, in the multitone case of dual-frequency approach (uplinkand downlink using distinct frequencies), the network uses a frequencydivision duplex (FDD) protocol, preferably in combination withchannel-based transmission and reception weights.

The network advantage to the preferred embodiment is that, instead ofthe network serving as a Procrustean bed to which the communicationslinks must be fitted out of the combination of its environmental SINR,established protocols, and channel approach, each communications linkcan use the environmental SINR, protocols, and channel approach todynamically adapt the network's functioning to maximize capacity andminimize power consumption.

The second preference is that the network uses and exploits diversityfrequency transmission and reception at all nodes in the network. Thisparticular aspect of the preferred embodiment carries the complexity andhardware cost of requiring that each node incorporate spatiallyseparated and shared receive/transmit antennae, although the separationneed only be measured in tens of lambdas of the lowest frequency(longest wavelength) used by that particular node. This pragmaticallycan create a situation where BS nodes are equipped with larger antennaewhich are spatially separated by feet or meters, and thus use far lowerfrequencies for ‘backhaul’ or BS to BS transmissions; this also carries,however, the advantage that SU nodes lacking such spatial separationwill be intrinsically deaf to such frequencies. (Care must be taken toconsider harmonics between BS backbone frequencies and SU channelfrequencies.)

Optionally, in an enhancement to the preferred embodiment, the networkwould include and make use of frequency polarization, spectraldiversity, or any combination thereof, at any subset (including a propersubset) of the network's nodes to provide further coding anddifferentiation potential. And in another, further enhancement, thenetwork would employ Butler RF networks to provide common RF front-endand scalable and expandable transceiver DSP backends in peer-to-peernetwork implementations.

The third preference is that the nodes include a multitone QAM encoder,whereby individual tones would be multiplied byQuadrature-Amplitude-Modulated symbols to further differentiate thesignals between nodes, even those using the same frequencies.Alternative QAM approaches would include PSK, π/4 QPSK, and π/4-DQPSKsymbols to increase the variation potentialities. These symbols would begenerated using Trellis-Coded-Modulation (TM) encoding over individualfrequency channels and would include several-to-one multitone symbols.One alternative embodiment would use Reed-Solomon codes and directmapping to symbols; other alternatives would use Turbo encoding, eitherat the baseband or as part of the TM, or any combination thereof. To aidthe Viterbi decoding at the receiver the ‘tail-biting approach would beused at the edge of the symbol blocks. To assist the maximum capacitysolution for each frequency channel, the network would use variableinformation bits per frequency channel rather than a fixed set ofinformation bits per frequency channel, in a method analogous to theDigital MultiTone, Digital Signal Loss (DMT DSL) approach, trading theneed for information density encoding as part of the signal overhead forthe need for all channels to be constrained to the minimum guaranteedcapacity of any environmentally or hardware constrained channel, toavoid pathloss for the most tightly constrained link or channel.However, rather than insist upon this approach, the network wouldinclude the capability to shift to a constant bits per frequency channelapproach with appropriate LEGO power management, to enable and supportthe minimum-power solution for the network when either power or capacityconstraints determine this is preferable.

The fourth preference is that the network adds pseudorandom modulationto the symbols after encoding. This is to eliminate the need to increasethe signaling overhead by runs of correlated symbols, as it aids in thereceive adaptation algorithms, provides discrete link encryption,thereby greatly increasing both channel and network security, andenables pilot-gated fast acquisition and timing recovery algorithms. Anextension to the pseudorandom modulation is the analysis and eliminationof certain detectable features by the network in one alternateembodiment. A distinct extension is the embedding of invariance forexploiting broader modulation, using gated SCORE. And a third extensioncombines the two extensions just described.

The fifth preference is the addition of an error detection syndrome, orCRC block to transmissions to detect bit errors, which would enable theinitiation of a retransmission request at the end of a packet'sreception when an error is signaled.

The sixth preference is using a computationally efficient andfast-converging receive weight algorithm (CE&FC RWA) to reduce thecomputational and hardware overhead for each channel's transmission andnode. Variations of such CE&FC RWA that would be used include any one,subcombination, or combination, of the following: Least-Squares Like(LS), which are also known as matrix-inversion; Block-Updateimplementations (on a per-packet basis) that amortize matrix operationsover multiple data snapshots (using tones and/or multitone symbols); andrecursive single-snapshot algorithms. Furthermore, the preferredembodiment may use one, more than one, or all of the following for thesame purposes: calculation of autocorrelation statistics in voltagedomain (e.g. using QRD, MGSO) to minimize the complexity and increasethe accuracy of the weight-update operation; multiport adaptation(simultaneous processing of multiple co-channel links) on each frequencychannel to amortize autocorrelation operations over multiple users (moreat BS than SU nodes); or single-step, single-port adaptations (more atSUs than BSs). Depending on the network constancy and dynamic state, orstatic constancy, the network may vary between uncalibrated techniqueswhich are not dependent upon knowledge or calculation of channelinformation (e.g. the emitter location, or the elevation/azimuthseparating transmitter and receiver), non-blind and blind weightadaptation techniques such as pilot-based initial weight acquisitionsignaling, blind and/or pilot-aided decision-direction weighting inpersistent links, and blind embedded-invariance techniques such as gatedSCORE, in an alternative embodiment. For pilot-aided and gated SCOREtechniques the network would preferentially use the computationallyefficient mechanization of cross-correlation operations employing fasttransform (and in a specific embodiment, FFT) methods. In yet otheralternative embodiments the network may use combined channel sounding,channel-based weight estimation, or any combination of the foregoing.

The seventh preference is using post-combining in-channel toneequalization to remove timing and carrier offset. This could includemultiplication by constant modulus weights, as the first preference, toremove timing and/or delay offsets; alternatively, it could includelow-complexity intertone filtering to remove carrier offset and Dopplererrors; and, of course, a combination of both could be employeddepending on the environmental and hardware complexity needs,constraints, and costs.

The eighth, and most important preference for the preferred embodimentis that each node of the network be capable of employing and employretrodirective transmission and reception modulation, wherein thetransmit gains are set proportional to the actually experiencedreception weights for the frequencies used. For single frequency links,this exploits their potential reciprocity (especially for TDD or ad-hocnetworks). When a TDD approach is used each data frame is encapsulatedin smaller guard frames, and the entire transmission occupies a smallerportion of the available bandwidth to similarly encapsulate it in theavailable bandwidth. The signal for a one-way frame duration is furtherbroken down to incorporate a guard time, a data symbol, an encapsulatingcyclic prefix, a control symbol, a cyclic prefix separation the controlsymbol from the acquisition symbol, and a final encapsulating cyclicprefix. The frequency channels that occupy the bandwidth carry bearerdata fragments over fractional subfragments. One embodiment forlow-mobility, fixed or portable TDD uses a 120 B Bearer Data Fragmentwhich is comprised of eight 15B subfragments, 8 differentiating andcoordinating multitone symbols, and of 5.75 MHz available only 4.26 MHzbandwidth, said bandwidth being divided into 13 frequency channels, eachwith 320 kHz, to provide 2 fragments per frame per link, or 6.24 Mbyteseach frame, or 4.608 Mbps as one channel (Channel 0) is reserved forfragment resends, thereby providing the equivalent in wirelesstransmission of 3 land-based T1 lines with, thanks to the reservationand resend provision, a 10⁻⁴ BER. This embodiment further uses for each15 B subfragment a MAC header providing 2 B CRC and 13 B MAC data,providing 52 MAC channels and at full duplex 10.4 kbps per channel. Theacquisition symbols have 30 B pilot or synchronization data per 320 kHzfrequency channel, 32 to 64 pilot tones per channel, and thereby providefast acquisition for up to 32 Degrees of Freedom; and if the area issparsely populated or for other reasons (downtime, occupied by othertransmissions) less than 17 Degrees of Freedom were needed, the excessDOF could be reused and reprovisioned to enable dynamic channels andthereby further increase the local flexibility.

The high-mobility TDD link replaces the cyclic prefixes with nulls onthe uplink and CP on the downlink, halves the number of tones anddoubles the separation of tones (from 426 to 213 tones, from 10 kHz to20 kHz separation), and provides half the DOF, but doubles the amount ofoverlap that can be tolerated for the same QOS.

In the preferred embodiment for fixed, portable, and low-mobility links,the tone layout divides the 4.26 MHz into 426 ‘bins’, each of 10 kHzseparation; these are then shared such that thirteen channels, each with32 tones covering 320 kHz, from Channel 0 to Channel 12 are formed, witheach channel further carrying of the 32 tones anetwork-information-bearing tone at the bottom and top of the channel(T0 and T31) that encapsulate the content-carrying tones T1 through T30.Each channel is modulated by a 32-tone pilot to facilitate theacquisition and fine time synchronization. The 10 kHz tone separationcontrols reasonable levels of time selective Multipath (+/−100 MHz),with a cyclic buffer being added at channel edges. The network can,should environmental or network conditions suggest, ‘step down’ theoverall frequency spread to 160 kHz BW without affecting the fundamentalstability of the traffic algorithms or network. (See FIG. 26)

However, for high-mobility TDD links the number of bins, pilot size, andinformation-bearing tones per channel are halved, while the toneseparation is doubled. This will permit high levels of Doppler shift(+/−5 kHz) without sacrificing QoS or content integrity; and again, thenetwork can step down the overall frequency spread and thus thebandwidth per bin can be halved (from 320 kHz to 160 kHz) withoutaffecting the fundamental stability of the traffic algorithms ornetwork. (See FIG. 27)

Preferentially transmit gains are set proportionally to the conjugate ofthe receive weights for that particular node and channel. An alternativeapproach uses channel-based retrodirective transmit gains (more for SUthan BS); a second alternative uses channel-based directive(beam-pointing) transmit gains (more for BS than SU); a third appliesretrodirection to in-channel preemphasis gains; and any combination ofthese alternative approaches may be employed. For any suchsingle-frequency link the transmitting node breaks periodically (in oneparticular alternative embodiment every 5 msec) to collect ACKs, NACKs,or RTSs, that is, to monitor the link performance as perceived by thereceiving node. This approach, though it provides all the capacity in aparticular link to a user as needed, is very compatible with small,stationary networks but less compatible with LEGO network management dueto the effects of nonstationary network fields.

The ninth preference is that the network employs Locally-Enabled NetworkOptimization (LEGO) to manage the transmit power for each node (BS andSU) operating, dynamically. This requires that relatively complexcomputational operations (e.g. receive weight and transmit gaincalculations, multitone, QAM, TCM, and the above-mentionedsignal/symbol/weight/frequency calculations) be carried out autonomouslyat each node in the network, rather than limited to one class of nodes.This further requires that as part of the network overhead simple,network-level control parameters be passed to, or shared by (for certaintime intervals, though such may either be hard-set invariances in thehardware, subject to change signal, or network-alterable) all nodes inthe network. Additionally, each node would implement itspower-management algorithm to minimize transmit power and manage itslinks, thereby indirectly optimizing performance over the entirenetwork. An alternative embodiment would effect network-leveloptimization; and a third would combine node-driven local determinedoptimization with network-level optimization.

Although the preferred embodiment uses an algorithm that presumes thatpower capacity will vary over the network, and that establishes localmaxima by favoring capacity maximization for the power constraint ateach particular node in the network (i.e. a goal driven minimizationalgorithm), various alternative LEGO algorithms could be employed. Forexample, if power shortfalls or constraints on any part of the networkare anticipated, then a capacity maximization subject to that powerconstraint algorithm could be used. A third alternative, presuming thatthe network capacity (as opposed to the power) is the guidingconstraint, sets the power minimization subject to the capacityattainment to the limit possible over the entire network. And a fourth,which is better, sets the power minimization at each particular node inthe network subject to the capacity constraint at that particular node.

The preferred embodiment incorporates into each node a multitone QAMdecoder, with a soft-optimized, Viterbi algorithm (SOVA) embodied in thedecoder, such that the network can effect changes in the decoder at itsnodes by a software or information transmission that re-sets thehardware (EEPROM, FPGA, PAL, or other semiconductor chip) and softwareat that node for the new decoding scheme. An alternative embodiment withlesser cost and complexity at each node, but lesser flexibility, is touse hard-optimized, Viterbi or Reed-Solomon, decoders at each node inthe network. A third alternative is to combine both SOVA and HOVAdecoders in the network and establish hierarchies wherein the moreflexible stations moderate as needed to communicate with their lessflexible but simple contacts.

The preferred embodiment of the present form of the invention alsoincorporates synchronization means for its communications, whichencompass timing estimation, carrier estimation, and synchronizationoperations as part of the network communication and controlmethodologies. The preferred synchronization is to a single, universal,and commonly observable timing signal such as that used in GPSoperations, and occurs as part of the carrier signal (also known as ‘UPSSync’). An alternative embodiment would use synchronization to a timing,carrier, or mutual offset which would be observed at the transmission ormaster node during the reception process, wherein the slaved receiversynchronization would introduce a x2 delay and carrier error at theslaved transmitter, to avoid interference with the master transmission.Another alternative embodiment would use precompensation (in timing,carrier advancement, or both), to equalize any timing or carrier offsetobserved at both ends of the link (a means to synchronize the slavednode's transmission). Combinations of universal, offset, orprecompensation synchronization methods would be yet further alternativeembodiments.

Synchronization would be performed by including in the transmissiondedicated multitone signals (such forming part of the set of QAM symbolsused by the preferred embodiment), or by using dedicated tones in eachmultitone symbol, or most preferentially, by combinations of dedicatedtones and slots to maximize the synchronization possible for the minimaltransmission density. Coarse synchronization would be performed prior tothe multitone demodulation, using the envelope features of the waveform.Fine synchronization would be performed after multitone demodulation,using dedicated QAM synchronization symbols and tones. For embodimentsusing universal observed timing through GPS synchronization, or usingslaved transmission synchronization, these would be performed usingcontrol or MAC channels. An alternative embodiment would bypass thesynchronization operation entirely by using GPS-based timing and carrieracquisition methods. A separate alternative embodiment would use blind,data-based synchronization methods, minimizing the use of specificsynchronization data.

In the preferred embodiment Transmit/Receive (T/R) compensation meansare employed to remove nonreciprocal channels after shared transmit orreceive operations. These would be employed intermittently on an‘as-needed’ basis through transmission of specialized T/R compensationpackets to initiate the compensation processing. An alternativeembodiment would use dedicated T/R compensation channels to initiate thecompensation processing. And the network would employ loops back of thereceived signal data to provide the initial transmitter with the T/Rchannel differences.

The preferred embodiment further includes methods for datagram networkinstantiations, particularly applicable for conditions such as edgenetworks (e.g. where the wireless electromagnetic communications networkis connecting to the Internet) and entirely interior networks (e.g. the‘backhaul’, dedicated, data-heavy, and often fiber-optic networks ofother carriers). These enable the transmission of data in discretedatagrams, or fragments of datagrams, over multiple routes, such asneighboring nodes, according to the availability of transmission channelcapacities. The recipient nodes would then reconstitute the originaldata stream from the received and re-ordered datagrams or datagramfragments. The preferred embodiment mechanizes the process byincorporating, or enabling, both TCP/IP and FTP protocols, and furtheruses fragment-level CRC's, error detection, and retransmission protocolsto provide Zero-error, Uncommitted Bit-Rate (ZE-UBR) services. By usingreservation protocols such as VoIP RSVP common to the industry, thepreferred embodiment can also provide Committed Bit-Rate (CBR) service.

The preferred embodiment also incorporates means for resolvingscheduling and capacity problems, preferentially the soft-contention andDemand-Assigned, Multiple-Access (DAMA) scheduling means. These wouldprimarily be employed at network edges, though they also can serve at‘bursty’ edge networks or handle ‘unconcentrated’ data streams. The softcontention means minimize the effects of data collisions and the latencydue to retransmissions and/or backoff network effects; the DAMAscheduling is principally employed over longer sessions to maximize thenetwork efficiency.

The topology of the wireless electromagnetic communications networkaffects the local details of implementation of the preferred embodiment,as different constraints and needs dictate how the best adaptationoccurs. For small network embodiments where most, if not all of thenodes are in a common field of view, it is not important whether thenetwork be in a star, ring, bus, or mesh topology. Under theseconditions the preferred embodiment matches each transceiver's Degreesof Freedom (DOF) to the nodes in the possible link directions andequalizes them to provide node-equivalent uplink and downlink capacity.An alternative embodiment may also be used, depending on network trafficor user payment/preferences, wherein asymmetric transceiver assignmentsreflect the desired capacity weighting. After the DOF matching iscompleted, each node adapts the Receive Weights to form a hard(max-SINR, null-steered) or soft (max SINR) solution for multipathresolution for transmissions to that node. Then explicit interferencewhitening for in-network nodes, or implicit data whitening for softnulling of out-of-network interferers are employed for conditions, e.g.as in Part 15 applications. Finally, retrodirective transmit gains(whitened or unwhitened as above) are used during subsequenttransmission operations during a channel communication. In analternative embodiments, the Receive Weights are directive, whitened,channel-based, or a combination thereof.

For large network embodiments the fundamental conditions are differentand thus a different implementation and adaptation strategy is usuallyrequired. These include mesh extensions of star, ring, and bus networks(see FIG. 4); and the principal difference is that most nodes are not ina common field of view. A greater amount of network ‘passing over’ isrequired and nodes must more often serve as intermediary rather thanterminal transceivers. Under these conditions, for each node thetransceiver DOF is matched to those nodes that are observable during theReceive operation, and then performs the symmetric equalization (oralternative, asymmetric equalization) and other operations (andalternatives) as described in the preceding paragraph. Under theseconditions the LEGO parameters used for management of the network aredisseminated throughout the network.

A third possible topology and concomitant operating conditions occurswhen the network is cellular, or has overlapping subordinate orcoordinating networks. Under these conditions, which are particularlylikely to occur under competition, not all the nodes will be connectedto the same wireless electromagnetic communications network. A greaterpotential for signal interference which is beyond the network's controlresults and the preferred embodiment adapts to this constraint.Furthermore, there may be some minimal transmissions of control, networkhealth, or network OAMP data through a disparate infrastructure (wiredor wireless), or even a high-rate connection through a wiredinfrastructure in an alternative embodiment.

When these conditions occur and networks are in view of each other, theycan experience signal or physical overlapping; in many situations, suchas urban areas, there may be heavy interpenetration. In principle therewill be physical separation of the disparate networks' nodes, at leastas to their geographic identity (two mobile phones generally do notshare the exact, same physical location and continue to work when socrushed together); in practice, however, all the networks' nodes sharethe same physical layer of the electromagnetic spectrum and the realworld geography and hardware. What ensures the continued separation ofthe networks is and enforced separation at the non-physical, informationand communication layer. Alternative embodiments may allow low-ratecommunication, or means for limited, or even allowable fullinter-network communication, depending on the differing networks'contract agreement as to communications and provisioning sharing.

Under these conditions the nodes in the preferred embodiment directnulls (hard or soft) at all observed Transmission nodes in othernetworks, to minimize the interference from and with the other network'ssignals. This same approach may be used, in an alternative embodiment,to enforce a ‘lock out’ of unauthorized nodes in a secure network. Thenulls are further enabled using network-wide scrambling, gating, orencryption means as described elsewhere, to differentiate the twonetworks' internal signals.

An alternative embodiment incorporates one or more broadcast modes fromnetwork master controllers, that would enforce common timing standardsand provide broadcast, i.e. network common information, withoutrequiring two-way bandwidth for such effort.

Advantages: LEGO

Among the advantages of the preferred embodiment's solution of the localoptimizations to obtain global optimization are the following: (1) theworking target capacity objective for any given set of nodes may berapidly reached by iterating from an initial approximation to anacceptably-constrained solution with many fewer iterations overall; (2)the power levels that solve the local target capacity objective minimizethe transmitted energy at the local node, thereby minimizing theco-channel interference to other uses in the network; and (3) thequantities needed for the local solution only require local informationto solve, thereby reducing substantially the ratio between ‘control’ and‘content’ information, thus further enhancing the overall capacity ofthe network.

Additional advantages of the preferred embodiment's LEGO approach are(1) that it can be solved at each node using a very simple but powerfulapproximation technique that converges rapidly to the correct solution;(2) the power levels, at each respective node, that solve Eq. 3 actuallyminimize the transmitted energy at each respective node, hence (3)minimizing co-channel interference to other users (nodes) in the networkwhile achieving the targeted capacity rates; and (4) most of thequantities required for the optimization only require local information.

Further advantages of the preferred embodiment include the lack of anyneed for estimating any channel matrices., and substantial lessening ofdetailed and fine calculation and recalculation of both (1) the initialSINR ratios and (2) the effect on each node of changing the power usageof any other node in the network. This latter has a secondary effect ofreducing the amount of ‘control’ information that needs to be sentacross the network, and reduces the amount of work or complexity thathas to be managed by the network controller.

LEGO Effect on the MIMO Network

By solving the optimization at the local level for each node of thenetwork, the problem of network optimization becomes a hierarchical one,where the overall problem is reduced to a series of subproblems. Thepreferred embodiment's implementation as described above, is thengeneralized to handle both the power constrained unconstrained(negligible noise) objective functions.

For the channel capacity value D₂₁, the network performs theoptimization

D₂₁=max β such that

$\begin{matrix}{{\beta \leq {\sum\limits_{q \in {U{(m)}}}{\sum\limits_{k}{\log \left( {1 + {\gamma \left( {k,q} \right)}} \right)}}}},{{\gamma \left( {k,q} \right)} \geq 0},{{\sum\limits_{m}{R_{1}(m)}} \leq R},{{\pi_{1}\left( {k,q} \right)} \geq 0},{{\sum\limits_{q \in {U{(m)}}}{\sum\limits_{k}{\pi_{1}\left( {k,q} \right)}}} \leq {R_{1}(m)}}} & {{EQ}.\mspace{14mu} 49}\end{matrix}$

where U(m) is a collection of links in a given aggregate set m, k is atransmission mode index, reflecting the fact that a single link maytransmit over multiple diversity channels, π₁(k,q) is the transmit powerfor mode k and link q, γ(k,q) is the post beamforming signal tointerference noise ration, and R₁(m) is the total allowed transmit powerfor aggregate set m; by solving first the reverse link power controlproblem; then treating the forward link problem in an identical fashion,substituting the subscripts 2 for 1. The solution for the link-leveloptimization as described above is then implemented at each node, andthe network solution derived therefrom.

The means used to solve D₂₁ optimization are chosen to minimize theamount of auxiliary channel information, or network control informationthat displaces network content. For most of the max-min objectivefunctions described herein, a necessary condition of optimality is thatall of the links over each link index q achieve the same capacity. Wecan therefore require the constraint in Eq. 49 to be an equality Forthis embodiment, the objective function that is solved at each aggregateset m, becomes:

${\min\limits_{\pi_{r}{(q)}}{\sum\limits_{q \in {Q{(m)}}}{\pi_{r}(q)}}},$

such that

β=Σ_(q∈Q(m))log(1+γ(q))  EQ. 50

The preferred embodiment linearizes this objective function as afunction of γ(q) and optimizes it using the formulation in EQ 28, EQ 29and EQ 30.For each aggregate set m, the network now attempts to achieve the givencapacity objective, β, by (1) optimizing the receive beamformers, usingsimple MMSE processing, to simultaneously optimize the SINR; (2) basedon the individual measured SINR for each q index, attempt toincrementally increase or lower its capacity as needed to match thecurrent target; and (3) step the power by a quantized small step in theappropriate direction. When all aggregate sets have achieved the currenttarget capacity, then the network can either increase the targetcapacity β, or add additional users (opportunistically or by signal) toexploit the now-known excess capacity. The network controller of thepreferred embodiment is computationally extremely simple and requires avery small feedback channel, or portion of the control channel otherwiseunused, to accomplish its tasks.

As the network evolves each independent channel is assigned a variablerate codec optimized for the currently achieved SINR for that link,whereby a code and associated rate are chosen to achieve the desired biterror using any of the Trellis Codes, interleavers, and/or Reed-Solomoncodes known to the literature.

Good network performance includes, generally, a measure of uniformminimum performance level for all links assigned the same quality ofservice (QoS). The preferred embodiment uses Max-Min capacity criterionas disclosed above to attain this, as it is generalizable to a widevariety of network configurations. Minimizing the total power subject toarbitrary capacity constraints β(m), as in EQ 3 and EQ 4 is also anembodiment of interest and is easily accommodated by the currentinvention. The addition of reciprocity as a feature of the preferredembodiment allows us to state the decoupled objective function:

$\begin{matrix}{{D_{rt} = {\max\limits_{\pi_{1}{({k,q})}}{\min\limits_{m}{{D_{rt}(m)}\mspace{14mu} {where}}}}}{{D_{rt}(m)} \equiv {\sum\limits_{q \in {U{(m)}}}{\sum\limits_{k}{\log \left( {1 + {\gamma \left( {k,q} \right)}} \right)}}}}} & {{EQ}.\mspace{14mu} 51}\end{matrix}$

as the largest possible mutual information that can be obtained, as theone to be used to obtain network optimality.

The reciprocity equation, Eq. 2, establishes that the network's uplinkcapacity will equal its downlink capacity provided that the receiveweights are used to transmit and the transmit weights to receive.Implementing the network optimization in this fashion provides thefollowing benefits: (1) transmit weights can be obtained from receiveweights; (2) transmit and receive weights require only local informationat each node, thereby eliminating the ‘network God’ and ‘commonknowledge’ problems; and (3) local optimization done using thisoptimizes the entire network, both making it stable and converging. Thereciprocity equation is used particularly to tell each node how tochoose its transmit weights optimally.

An improvement over blindly substituting transmit and receive weights,however, is in using the proper form of the objective function thatsatisfies the reciprocity equation, for that determines how to optimallyadjust and select the gain over multiple outputs and multiple inputs.The choice of the objective function specified above dictates thealgorithmic procedure that is also specified above to optimize thenetwork.

Alternative embodiments of the network controller include having it setthe entire network target capacity objective (β), using the networkcontroller to add a node, drop a node, or change the target capacityobjective for the nodes it governs or the network. FIG. 41 illustratesone feasible algorithm whereby a new node (or a node which had earlierdropped out of the network) enters the network. Further embodimentsinclude using a network control element that may, either in addition toor in replacement for altering β, add, drop, or change channels betweennodes, frequencies, coding, security, or protocols, polarizations, ortraffic density allocations usable by a particular node or channel. Inyet another embodiment the network control element selects and managesdiffering constraints, not being limited just to power and capacity, butalso QoS, the amount of frequency spread between channels, the multipathdensity allocated to any particular pairing of nodes, or to anyparticular user, or any combination and subcombination of all of theabove.

Although the present invention has been described chiefly in terms ofthe presently preferred embodiment, it is to be understood that thedisclosure is not to be interpreted as limiting. Various alterations andmodifications will no doubt become apparent to those skilled in the artafter having read the above disclosure. Such modifications may involveother features which are already known in the design, manufacture anduse of wireless electromagnetic communications networks, systems andMIMO networks and systems therefore, and which may be used instead of orin addition to features already described herein. The algorithms andequations herein are not limiting but instructive of the embodiment ofthe invention, and variations which are readily derived throughprogramming or mathematical transformations which are standard or knownto the appropriate art are not excluded by omission. Accordingly, it isintended that the appended claims are interpreted as covering allalterations and modifications as fall within the true spirit and scopeof the invention in light of the prior art.

Additionally, although claims have been formulated in this applicationto particular combinations of elements, it should be understood that thescope of the disclosure of the present application also includes anysingle novel element or any novel combination of elements disclosedherein, either explicitly or implicitly, whether or not it relates tothe same invention as presently claimed in any claim and whether or notit mitigates any or all of the same technical problems as does thepresent invention. The applicants hereby give notice that new claims maybe formulated to such features and/or combinations of such featuresduring the prosecution of the present application or of any furtherapplication derived therefrom.

FIG. 42 depicts the baseline transceiver employed in the primaryembodiment. The radio consists of a pair of antennas and RF transceivers(frequency up/down converters and PA's); a digital ASIC, FPGA, orsoftware radio component to implement lower-PHY (LPHY) symbolmodulation/demodulation operations, linear combining of LPHY modemoutput signals during receive operations and corresponding linearweighting and distribution of LPHY model input signals during transmitoperations; an LO employing a GPS disciplined oscillator (GPS-DO); and asoftware computer to implement optional higher-layer codec andcollaborative radio applications. Typical commercially availableGPS-DO's can provide <100 ns relative node-to-node timing error (wellwithin the 800 ns delay error budget provided by the lower PHYmodulation format) and <1 Hz carrier offset error (separate from Dopplershift between nodes), stable to within 300 ns over periods of >1 hour inevent of GPS outage, e.g., due to foliage or man-made obstructions. InTDD network instantiations, this can allow fast (1.25 ms) node entry tothe network without the need for detailed and distributed timingsynchronization mechanisms. In absence of GPS-DO's collaborative meanscan be implemented to allow the radios to synchronize to a common sharedtime reference by monitoring broadcast signals transmitted from adjacenttransceivers, or to synchronize to GPS time and frequency standards ifjust one of the transceivers in the network possesses a GPS-DO, allowingthe transceivers to be implemented at greatly reduced cost. The baselinetransceiver can to employ two degrees of freedom during receive andsubsequent transmit operations, to set up independent ports to up to twoother transceivers in the network during transmit or receive operations,or to null one external interferer during receive operations. Antennasin the network can be polarization diverse (transmitting and receivingon linearly independent, preferably orthogonal polarizations), spatiallydiverse (deployed at spatially separated locations), or combinations ofpolarization and spatially diverse. It is also possible to have a singlechannel transceiver capable of communicating with the network, or anynumber of ad-hoc combinations of transceivers of varying capabilities,likewise.

FIG. 43 depicts means for extending the baseline transceiver to largernumbers of antenna channels. The baseline transceiver and adaptationalgorithm include provisions for routing of transmit and receive dataand a modified Gram-Schmidt orthogonalization (MGSO) statistics betweenboards, in order to allow the radio to be scaled up to as many as 8antennas by combining 2, 3, or 4 Transceiver boards together. Atransceiver employing L baseline transceivers can employ up to 2Ldegrees of freedom during receive and subsequent transmit operations, toestablish independent channels to up to 2L separate transceivers in anetwork, or to excise up to 2L−1 external interference during receiveoperations.

FIG. 44 depicts an exemplary MIMO networking routing scenario in whichfour transceivers with four spatial degrees of freedom are used to routedata around a four-node network in the presence of a strong jammer. Eachtransceiver employs two of its four available degrees of freedom toestablish connections with its neighboring transceivers. The remainingdegrees of freedom are then used to excise the jammer in the center ofthe network, and to increase signal-to-noise ratio (SNR) in thedirection of its neighboring transceivers. In a time-division duplex(TDD) communication network, these connections can be used tosimultaneously route data in clockwise and counter-clockwise directionsto form a counter rotating ring network. In lightly loaded networks,this can double the capacity available to any node in the network;moreover, this can greatly increase availability of the network to eachnode, by providing a redundant path for transmission of data betweennetwork ports.

FIG. 45 depicts the baseline symbol and timing structure allowingtransceivers to connect with each other in a symmetric time-divisionduplex (TDD) multinode network. Packets are transmitted to and fromuplink receive nodes and downlink receive nodes during alternating timeslots, with an appropriate guard time, e.g., for switching betweentransmit and receive modes, transmission of signaling/control andnetwork maintenance/provision packets, computation of receiver CRC's anddecoding algorithms, and operation of higher-layer data routingprocedures. Within each slot, an integer number of lower PHY (LPHY)symbols is transmitted. The number of symbols is chosen to have a valuethat allows implementation of efficient orthogonal transformations overthe symbol time index. In the primary embodiment, this number is setequal to a power-of-two, e.g., 256 symbols, in order to use of fastHadamard transform (FHT) operations; however, other embodiments canemploy different numbers of symbols, e.g., allowing implementation ofmixed-radix fast Fourier transforms (FFT's) or other linear orthogonaloperations. Each LPHY symbol is assumed to possess a base symbol and acyclic prefix that is discarded during the LPHY symbol demodulationoperation, allowing the transceivers to be insensitive to delay andmultipath with maximum substantive value that is less than the cyclicprefix. In the preferred embodiment, the cyclic prefix and base symbolis set to 800 ns and 3.2 μs, commensurate with the 802.11 OFDM trafficPHY, and may include additional synchronization and signaling symbols(external to symbols carrying traffic information) in order to maximizecommonality with 802.11 hardware, and/or promote eventual coexistenceand/or integration of the invention into 802.11 networks. In thepreferred embodiment, the LPHY symbol is also either an OFDM waveformcomprising multiple subcarriers (modulated OFDM tones) or a PAM signalequivalent to a single carrier of an OFDM waveform, e.g., for low-rateapplications. However, the invention is compatible with many differentLPHY modulation formats besides OFDM or PAM, including spread spectrummodulation formats that spread the signal over wide bandwidth, andLPI/LPD modulation formats that reduce or eliminate cyclic features ofthe waveform.

FIG. 46 depicts exemplary means for deploying the preferred embodimentin a TDD mesh network topology, in order to transmit data between awidely separated source and destination node. In this embodiment, thenetwork nodes are first separated into uplink transmit nodes (downlinkreceive nodes), depicted with light interiors in FIG. 46, which transmitdata over even time slots and receive data over odd time slots, anduplink receive nodes (downlink transmit nodes), depicted with darkinteriors in FIG. 46, which receive data over even time slots andtransmit data over odd time slots. Each transceiver is assumed topossess sufficient spatial channels to allow it to communicatesimultaneously with at least two of its nearest neighbors over each timeinterval, i.e., to form two links between its nearest neighbors in thenetwork, suppress any network or external interference impinging on thenodes, and close each individual link under channel propagationconditions observed by that node. At high spectral efficiency, e.g., inapplications where the nodes must transmit data at high rate, each node(and especially internal nodes) may require as many as eight antennas,or four baseline transceivers, to support MIMO networking communication.Conversely, in applications where the nodes must transmit data at modestrates, e.g., VoIP communications, each transceiver could operate with asfew as two diversity channels, or a single baseline transceiver, tosupport needs of the network.

Each link is given a unique link address (LA), defined by the transmitnode address (TNA) and receive node address (RNA), i.e., the nodeaddress (NA) of the nodes originating and terminating that link, and thechannel rank of that particular link if a true multirank MIMO linkexists and is exploited by those nodes. In the sixteen-node networkshown in FIG. 46, each LA is defined as a two-hex address comprising the(RNA,TNA) for that link, with a third hex number (equal to zero in everyLA shown in the Figure) reserved to capture the mode index (0=dominantrank) of that link. In FIG. 46, uplinks instantiated over odd time-slotsand passing data from uplink transmit nodes to downlink receive nodesare depicted as solid arrows, and downlinks instantiated over even timeslots and passing data from downlink transmit nodes to downlink receivenodes are depicted as dashed arrows. In this Figure, packets can betransported across the entire network in 7 time slots (3.5 TDD frames).

FIG. 47 describes the baseline symbol and timing structure for theinvention, when transceivers are operated in an ad-hoc multinodenetwork. In the preferred ad hoc embodiment, traffic data is transmittedover a preset numbers of LPHY symbols, e.g., determined at the beginningof data communications, which is larger than a minimum number of signalsN_(embed) determined by the overhead structure set aside to implementreceive adaptation algorithms at each transceiver, and which is aconvenient number, e.g., power-of-two, allowing implementation ofefficient data transformations in subsequent processing steps. However,the preferred ad hoc embodiment employs an overhead structure thatallows implementation of alternate methods that do not require exactknowledge of the full packet length to implement receive adaptationalgorithms, and that can allow transmission of packets of differentlength to different nodes in the network.

For certain levels of quality-of-service, the traffic packet may befollowed by an acknowledgment packet sent from the receive node(s) backto the transmit node, comprising N_(embed) LPHY symbols. As in the TDDinstantiation, the traffic and acknowledgement packets are separated bya guard time interval; however, the time interval between traffic andacknowledgement packets may be very short, e.g., on the order of theShort Interframe Space (SIFS) in 802.11 communications.

FIG. 48 depicts exemplary means for deploying the invention in an ad hocmesh network topology. In this network, any node can communicate withany other node in its field of view, allowing the source node tosimultaneously transmit packets to any available node in the network. InFIG. 48, for example, the source node transmits packets to five separateintermediate (relay) receive nodes over a first traffic time slot, anddirects those nodes to transmit packets directly to the destination nodeover a subsequent traffic time slot. In the preferred ad hoc embodiment,each of the relay nodes receive independent data, e.g., one of fivesubsets of data, from the source nodes, similar to the approach employedin the TDD embodiment. However, the invention supports an additionalmacrodiverse mode in which the source node transmits identical trafficdata to each intermediate node. In this case, the intermediate nodeswill form a macrodiverse transmitter that can exploit the full networktransfer function between the source node and intermediate nodes duringthe first traffic time slot, and the full network transfer functionbetween the relay nodes and the destination node over the second traffictime slot.

FIG. 49 describes multiport PHY transmit operations performed attransceivers using the invention; and these are described in detailbelow. In order to minimize complexity of the Figure, operations areshown for a single-carrier LPHY modulation format, e.g., a PAM formatemploying a cyclic prefix as shown in FIG. 45 and FIG. 47, or for asingle subcarrier of an OFDM LPHY. In the latter case, all operationsare replicated across subcarriers, with possible exception ofsubcarriers that may be reserved for synchronization purposes or forcompatibility with existing wireless air interfaces, e.g., 802.11ag, andadditional variations that may be introduced to increase security of thenetwork.

On the traffic path, data bits intended for each of M_(port) receivenode are first encoded into N_(data) complex traffic data symbols, e.g.,complex QPSK or QAM symbols, using conventional encoder technology.These symbols are then multiplexed onto the upper N_(data) input bins ofan efficient orthogonal transform operator such as a fast Hadamardtransform (FHT), such that at least N_(embed) bins of the transformationare not modulated by data during the subsequent transform operation. Onthe overhead or “pilot” path, at least one of N_(embed) bins ismodulated. This bin location is chosen based on the node address of thetransmit node, and a network-wide (e.g., time-of-day based) TransmissionSecurity (TRANSEC) operation known to each node in the network, e.g., bymodulo-N_(embed) adding the TNA to a common TRANSEC word, such that eachnode is modulating a unique bin during any given traffic oracknowledgement time slot. Each port of modulated traffic and pilotsymbols are then passed through the fast orthogonal transformation,yielding N_(FHT) transformed output symbols. Each port of transformedoutput symbols are then multiplied by a second, pseudorandom constantmodulus TRANSEC receive code based on the node address of the receivenode that the transmit node is attempting to communicate with over thatport and time slot, and other information known only to the networkusers. The resultant symbols are then multiplied by the M_(port)×M_(ant)transmit diversity weights employed at the transmitter, where M_(ant) isthe number of antennas employed at the transmitter. If those weights aredetermined adaptively, e.g., using knowledge of reciprocity of thecommunication channel, this data is further multiplied by a set oftransmit-receive compensation weights that enforce reciprocity betweenthe transmit and receive channels at the node. These symbols are thenpassed through the LPHY symbol modulator (in combination with symbolscorresponding to other subcarriers if the transceiver employs an OFDMLPHY), and onto the subsequent DAC, upconversion, power amplification,and RF transmission operations.

FIG. 50 describes multiport PHY receive operations performed attransceivers using the invention; and these are described in detailbelow. In order to minimize complexity of the Figure, operations areshown for a single-carrier LPHY modulation format, e.g., a PAM formatemploying a cyclic prefix as shown in FIG. 45 and FIG. 47, or for asingle subcarrier of an OFDM LPHY. In the latter case, all operationsare replicated across subcarriers, with possible exception ofsubcarriers that may be reserved for synchronization purposes or forcompatibility with existing wireless air interfaces, e.g., 802.11ag, andadditional variations that may be introduced to increase security of thenetwork.

After RF reception, downconversion, and ADC operations on M_(ant)spatial or polarization diverse antennas, data received at each antennais passed through an LPHY demodulator that converts that data toN_(FHT)×.M_(ant) complex matrix representation (M_(ant) columns ofN_(FHT)×1 complex data), on each subcarrier if the transceiver isemploying an OFDM LPHY. Each N_(FHT)×1 complex data vector is thenmultiplied by the conjugate of the TRANSEC receive code for that node,which removes that TRANSEC receive code added at the transmitter forthat port (and only for ports that were intended to pass data to thatreceive node). This data is then passed through the inverse of theorthogonal transformation employed at the transmitter, e.g., an inverseFHT, and separated into the lower N_(embed) output bins (anN_(embed)×M_(ant) complex pilot data matrix) corresponding to the pilotsignal(s) employed at each transmit node attempting to communicate withthat receive node, and N_(data) output bins (an N_(data)×M_(ant) complextraffic data matrix) corresponding to the traffic data transmitted tothe receive node, as well as interference generated by other nodes inthe network or external emitters.

On the pilot path, the N_(embed)×M_(ant) complex pilot data matrix isthen passed to an adaptation algorithm that detects bins modulated bythe transmitters attempting to communicate with the receiver; identifiesthose transmitters based on the detected bins and the TRANSEC transmitcode algorithm employed in the network, determines quality of thereceived pilot symbols and (by extension) traffic data, and developscombining weights that can extract the traffic data from theN_(data)×M_(ant) complex traffic data matrix at the maximumsignal-to-interference-and-noise ratio (max-SINR) achievable by thetransceiver. On the training path, these combiner weights are thenapplied to the traffic data matrix, and the extracted data is thenpassed to a traffic decoder that decodes the traffic data back into bitsand performs additional operations employed by the communication link,e.g., bit-level data decryption and error detection operations.

In the preferred TDD and ad hoc embodiments, each transmitter uses anorthogonal transformation of the same length during its transmitoperation, generating packets of the same time duration as well.However, in some alternate embodiments different transmitters maytransmit signals with different numbers of traffic data symbols. In thiscase, if an appropriate orthogonal transformation is employed at thetransmitters, e.g., a radix-2 FHT, and the pilot symbols are restrictedto an appropriate subset of input bins in that transformation, e.g., thefirst N_(embed)=2^(p) bins of an FHT, then the first N_(embed) symbols(or a multiple of the first N_(embed) symbols) can be used in thereceive adaptation algorithm. This can allow the invention to be used infully adhoc networks where nodes can transmit packets of arbitrarylength. However, receive combiner weights obtained through this processmay exhibit misadjustment relative to optimal weights for the trafficdata, as the pilot symbols may not experience the full processing gainof the FHT.

FIG. 51 describes multiport PHY receive adaptation operations performedin the primary embodiment of the invention; and these are described indetail below. On each subcarrier, the N_(embed)×M_(ant) complex pilotdata matrix is first passed to a whitening operation such as a modifiedGram-Schmidt orthogonalization (MGSO) or other QR decomposition (QRD)that separates the pilot data X into an N_(embed)×M_(ant) whitened dataset satisfying Q^(H)Q=I, and an M_(ant)×M_(ant) statistic vector R thatcaptures the autocorrelation of X, e.g., the Cholesky decomposition ofX^(H)X. The Q matrix is then analyzed (across multiple subcarriers forOFDM LPHY's) to detect all of the modulated pilot bins. This informationis used to unambiguously determine the TNA of each node attempting tocommunicate with the receiver. Once this determination has been made,the link SINR, whitened linear combiner weights, and subsequentunwhitened combiner weights are computed for each transmitted signal(and subcarrier for OFDM LPHY's).

FIG. 52 describes receive packet detection, address association, andlink SINR estimation performed in the primary embodiment of theinvention; and these are described in detail below. The adaptationalgorithms are uncalibrated, i.e., they do not exploit knowledge of theshaping, polarization, or physical placement of antennas used by thetransceiver, and can be used to instantiate arbitrary networktopologies, including point-to-point links, star networks, ringnetworks, or full mesh networks. When deployed in point-to-point links,successive iterations of the transmit and receive adaptation algorithmcauses each transceiver to adapt its multiport combiner and distributionweights to the eigenmodes (left and right eigenvectors) of their MIMOinternode channel response, typically in 2-to-4 TDD frames (5-10 ms).The resultant fully adaptive link can approach the Shannon capacity ofthe MIMO communication channel, regardless of the rank or distributionof the eigenvalues of that channel

FIG. 53 describes transmit adaptation algorithms performed in theprimary embodiment of the invention. The baseline system employsnetwork-wide collaborative link optimization rules referred to in [2] aslocally enabled global optimization (LEGO), which optimize network-widemeasures of network quality using retrodirective transmit weightadaptation and local (link layer) power management instructions. Theapproach exploits the ability to form nulls during transmit operationsto intelligently manage interference presented to other nodestransmitting or communicating in the same frequency channel. Thisincludes other nodes operating on the same network, and nodes operatingon disconnected networks, without the need for higher-layercross-communication between interference nodes or networks.

Also described below is also describe an alternate embodiment in whichthe adaptive receive combining weights are adapted to completelyseparate intended links with maximum signal-to-interference ratio ratherthan SIR, i.e., to direct “hard nulls” at each transmitter attempting tocommunicate with the node during the receive time slot so that interlinkinterference is completely removed during the receive operation, and todirect corresponding hard nulls back at the intended links duringsubsequent transmission opportunities. Although this algorithm doesintroduce some misadjustment (particularly during receive operations),it can improve the stability of the LEGO transmit adaptation algorithmsin highly dynamic communication networks.

The resultant fully-adaptive system provides a much more versatilesolution than competing multilayer receive-adaptive techniques such asBLAST and STP, which only approaches the Shannon capacity in full-rank(i.e., high multipath) channels. In particular, the fully-adaptivesystem can use its diversity degrees of freedom (DoF's) to providesubstantive transmit gain in low-rank channels found in airborne andrural conditions. The fully-adaptive system can also use these DoF's toexcise interference impinging on either end of the link due to othernodes operating in the same frequency band, hostile jamming of the link.In addition, the fully adaptive system provides an automatic powercontrol mechanism (LEGO Algorithm) that can be used to maximize capacity(high throughput applications) or minimize transmit power (LPDapplications), depending on the requirements of the system at any pointduring a mission. These attributes greatly increase the flexibility,range of operation, and application of the approach.

Moreover, the weight adaptation procedure is decoupled from operationsused to encode data onto each transmitter port (channel eigenmode) anddecode data taken from the corresponding receive port at the other sideof the link, without expensive multilayer decoding, encoding, andcancellation procedures employed in receive-adaptive methods. Thisgreatly reduces the complexity and reaction time of the transceiver, byallowing the transmit/receive weights to quickly adjust to highlydynamic environments encountered in military use scenarios.

The fully adaptive point-to-point link seamlessly extends tocollaborative multimode networks. In particular, each transceiveremploys the same multiport linear processor structure to formsimultaneous links to multiple neighbors in multinode networks. Theresultant MIMO networking strategy (first disclosed in [1]) exploits theadditional inherent route diversity of star, ring, or mesh networks, toprovide the benefits of MIMO processing in a network setting.

The MIMO network illustrated in FIG. 44 displays a minimalist-model,four-node, network that is operating in the presence of a jammer. Inthis figure, nodes employ the multiport linear receive and transmitoperations discussed above, to establish multiple simultaneous linkswith their neighbors. In the presence of point-to-point link diversity(high-rank MIMO channel response between communicators), e.g.,polarization or multipath diversity, nodes may establish multipleconnections between their neighbors. In the absence of such linkdiversity, or if desired by the network, the nodes may alternatelyestablish simultaneous connections with their neighbors. Theseconnections can be used to directly source data to these neighbors, ortransmit data through these neighbors to more distant destination nodes.In the example shown in FIG. 46, this strategy doubles the amount ofdata transportable to or from any node in the network—even if no linkdiversity exists on any of the internode channels (rank-1 internodechannel response). More generally, this strategy allows the capacity ofeach node to grow linearly with the number of antennas at that node,even in nondiverse communication scenarios such as airborne systems,desert combat scenarios, and ship-to-ship/shore naval communicationnetworks, while maintaining the ability to provide superlinearthroughput increases at low power levels. Moreover, this strategy allowsunused DoFs to be used for other purposes, including excision of strongjamming likely to be encountered in battlefield conditions.

The symbol and timing structure for the baseline Phase 1 Lower-PHY(LPHY) is illustrated in FIG. 45. In order to simplify the Phase I lowerPHY and requisite RF transceiver hardware, the method and system canemploy a linear PAM LPHY with a 250 kHz symbol rate (4 μs lower PHYsymbol period). In FIG. 45, the PAM symbol shape is assumed to be a 4 μsrectangle comprising a 3.2 .mu.s base pulse, used during demodulationoperations, and an 800 ns cyclic prefix that is discarded duringdemodulation operations. The resulting LPHY modem can be consequently bemodeled as a single subcarrier of an 802.11g-like OFDM symbol, allowingthe radio to be easily scaled to much wider bandwidths in later programphases. In addition, many other linear-PAM LPHY's are also consistent,including spread spectrum modulation formats that spread the signal overwide bandwidth, and LPI/LPD modulation formats that reduce or eliminatecyclic features of the waveform. The nominal ADC and DAC rates for thetransceivers can be 5 Msps, well within the capability of low SWaP/costsystems.

The multiport PHY transmit/receive operations are illustrated in FIG.47. At the transmitter, a frame of data intended for each transmit portis encoded to QAM using forward error correcting (FEC) encoding,organized into blocks of 240 QAM symbols, multiplexed with a transmitpilot that is unique to each node or transmit node address (TNA) in thenetwork, and spread over the frame and PHY subcarriers using a fastHadamard transform (FHT). The transmit pilot is designed to allowimplementation of computationally efficient adaptive detection andreception algorithms at the intended receiver(s) in the network, withoutthe need for prior knowledge of that pilot at the receiver. SixteenHadamard bins (software provisionable at each node based on the numberof antennas employed by that node) set aside for pilot transmission addonly 6.25% ( 16/256) overhead to the communications network, allowingeffective node detection, receive adaptation, and node data extractionin one TDD slot (1.25 ms), with as many as eight antennas pertransceiver. However, the number of pilot bins can be easily modified insoftware, or even provisioned on a dynamic basis, e.g., to improve theperformance of receive adaptation algorithms when more complextransceivers enter the network, or to increase link throughput as thoseradios leave the environment.

After the FHT, the combined information and transmit pilot are modulatedby a pseudorandom receive TRANSEC pilot, unique to the intended receiveror receive node address (RNA) in the network. If needed, the TRANSECpilot is also frequency compensated to remove Doppler shift anticipatedat the intended receiver, allowing the link to operate effectivelyvelocities much higher than those anticipated in the Phase I demo. TheTRANSEC output data is then passed through a linear distribution network(transmit beamformer) that can place beams in the direction of up toM_(ANT) targeted receive nodes for a transceiver with M_(ANT) transmitantennas. Alternately, the transmit beamformer can place up to 20log₁₀(M_(ANT)) energy in the direction of a single targeted receivenode, allowing effective data transfer at a much lower power andconsequent intercept footprint.

At the receiver, the processor strips off the receive TRANSEC pilot,simultaneously scrambling signals from any unauthorized user, andrevealing the unique transmit pilots from each authorized userattempting to contact the receiver during that receive frame. Theresultant data is passed to a computationally efficient, ModifiedGraham-Schmidt Orthogonalization (MGOS) based joint detection and signalextraction processor, which simultaneously detects each authorized pilotin the environment, and develops multiport receive combiner weights thatexcise interference in the communication channel (includingself-interference from other authorized users). These weights aredewhitened and applied to the information-bearing signal after theinverse FHT (IFHT), and used to adapt retrodirective distributionweights used during subsequent transmit operations. The combined receiveTRANSEC and IFHT spreading operations guarantees that the receivealgorithm will apply equal interference rejection to the pilot and datasymbols, for any interferer impinging on a node during its receive timeslot.

The baseline system employs network-wide collaborative link optimizationrules referred to as locally enabled global optimization (LEGO), whichoptimize network-wide measures of network quality using retrodirectivetransmit weight adaptation and local (link layer) power managementinstructions. The approach exploits the ability to form nulls duringtransmit operations to intelligently manage interference presented toother nodes transmitting or communicating in the same frequency channel.This includes other nodes operating on the same network, and nodesoperating on disconnected networks, without the need for higher-layercross-communication between interference nodes or networks. Thiscapability, originally developed for optimization of point-to-multipointcellular networks in wireless local-loop (AT&T Wireless Project Angel)and wireless metropolitan area networks (IEEE 802.16), cannot beemployed in systems that do not perform transmit adaptation. This methodand system can extend the LEGO approach to game theoretic methods thatmay perform this network optimization over a wider range ofapplications, networks, and optimization criteria.

The complexity of the method and system are shown in FIGS. 49-53. Systemcomplexity can be divided into two components: an FPGA corewarecomponent, comprising regular operations that are easily implementedusing field-programmable gate arrays or ASIC's, and a DSP softwarecomponent. FPGA coreware operations can include the lower PHY modem,transmit and receive beamforming (linear distribution and combiningoperations), transmit/receive TRANSEC operations, and FHT/IFHToperations. The DSP software operations can include the MGSO operation,node discovery, and weight dewhitening operation. In both such cases,complexity is calculated in DSP clock cycles per second, for ahypothetical DSP hardware element that can compute a simultaneous realadd and a real multiply in 4/3 clock cycle (1 clock cycle with 33%derating for memory transfer operations). Complexity is further dividedby the number of transceiver ports (simultaneous transmit and receivechannels), to provide a measure of the DSP cost of each link usedaccessed by the transceiver.

The overall complexity of the DSP component of transceiver operations(adaptive algorithm) is less than 200 kcps for the phase 1 system, orwell within the capabilities of a low-cost DSP components. Similarly,the overall complexity of the FPGA component of transceiver operationsis less than 30 Mcps, which easily fits on the commercially availableFPGA and in fact can be implemented using moderate cost DSP components.

Assuming low power rate-1 BPSK QAM encoding on each link, the resultantsystem can provide a PHY throughput (data rate into the MAC layer) of 96kbps (192 kbps full duplex), or establish 12 simultaneous 48 kbpsfull-duplex data links (576 kbps network transfer rate) between eachnode-pair in a four-node ring network, using a single two-channeltransceiver at each node in that network and a simple 250 ksps PAM lowerPHY. Assuming an active bandwidth of 250 kHz, this corresponds to aspectral network efficiency of over 2 bps/Hz. This efficiency andbandwidth scales linearly with the number bits/symbol employed in theQAM encoding operation. Because the ring network establishes acounterrotating ring that can transfer data over two simultaneousroutes, the reliability of the collaborative network increasesdramatically—by over 2 nines if each node in the network as a two ninesreliability!

DETAILED EMBODIMENT DESCRIPTION Parameter Definitions, Equations andGlossary Network Parameters:

Network parameters: Defaut Index # M_(NA) = Number of node addresses(active nodes) in the Variable (1.1.01) network M_(LA) = Number of linkaddresses (active links) in the Variable (1.1.02) network M_(PA) =Number of network-layer port addresses in the M_(NA)(M_(NA)-1) (1.1.03)network M_(RA) = Number of network-layer route addresses in the Variable(1.1.04) network Range Network addresses: m_(NA) = Node address (NA) 1:M_(NA) (1.1.05) m_(TNA) = Transmit Node address (TNA) 1: M_(NA) (1.1.06)m_(RNA) = Receive Node address (RNA) 1: M_(NA) (1.1.07) m_(SNA) = SourceNode address (SNA) 1: M_(NA) (1.1.08) m_(DNA) = Destination Node address(DNA) 1: M_(NA) (1.1.09) m_(LA) = Link address (LA) 1: M_(LA) (1.1.10)m_(RLA) = Return link address (RLA) 1: M_(LA) (1.1.11) m_(PA) =Network-layer port address (PA) 1: M_(PA) (1.1.12) m_(RA) =Network-layer route address (PA) 1: M_(PA) (1.1.13) Network mappings:μ_(NA)(m_(LA)) = [m_(TNA) m_(RNA)] connected by link m_(LA). (1.1.14)Alternate notation μ_(TNA)(m_(LA)), μ_(RNA)(m_(LA)) can also be used torefer to individual elements of μ_(NA). μ_(TDD)(m_(LA)) = TDD wubframeused by link m_(LA) (μ_(TNA)(m_(LA)) (1.1.15) transmits over TDDsubframe μ_(TDD)(m_(LA))) μ_(LA)(m_(TNA,) m_(RNA)) = Address of linkconnecting TNA m_(TNA) (1.1.16) to RNA m_(RNA) (0 if no LA)μ_(NA)(m_(LA)) = [m_(TNA) m_(RNA)] 

μ_(LA)(m_(RNA), m_(TNA)) = m_(LA) μ_(RLA)(m_(LA)) = Return link address(RLA) for link m_(LA): (1.1.17) μ_(NA)(m_(LA)) = [m_(TNA) m_(RNA)] 

μ_(NA)(μ_(RLA)(m_(LA))) = [m_(RNA) m_(TNA)] k_(TNA)(m_(frame)) = Adaptbin used by TNA m_(TNA) (same for all (1.1.18) links emanating fromm_(TNA)) over frame m_(frame). Same for all links emanating fromm_(TNA).

Datalink Parameters:

Default Address-independent datalink parameters: N_(codec) = Maximumdata encoding rate (can be noninteger) 8 (1.2.01) N_(FHT) = Hadamardbins per frame 256 (1.2.02) N_(embed) = Hadamard bins reserved foradaptation (≦N_(FHT)) 16 (1.2.04) M_(embed) = Modulated bins per port(≦N_(embed)/N_(port)) 16 (1.2.05) N_(data) = Hadamard bins reserved fordata (≦N_(FHT) − N_(embed)) 240 (1.2.03) M_(data) = Hadamard binsmodulated by data (≦N_(data)) 240 (1.2.03a) N_(sub) = Subcarriers perOFDM symbol 64 (1.2.06) M_(sub) = Modulated subcarriers per OFDM symbol52 (1.2.07) M_(QAM) = QAM data symbols per physical data frame 12,480(1.2.08) N_(OFDM) = OFDM symbols per physical data frame 312 (1.2.09)M_(OFDM) = Modulated OFDM symbols per PPDU 256 (1.2.10) N_(TDD) = Dataframes (TDD subframes) per TDD frame 2 (1.2.11) T_(FFT)(μs) = Durationof OFDM FFT in microseconds (μs) 3.2 μs (1.2.12) T_(symbol)(μs) =Duration of OFDM symbol in μs 4 μs (1.2.13) T_(prefix)(μs) = Duration ofOFDM cyclic prefix (guard interval) in μs 0.8 μs (1.2.14) T_(PPDU)(μs) =Duration of data PPDU in μs 1,024 μs (1.2.15) T_(frame)(μs) = Durationof data frame in μs 1,250 μs (1.2.16) T_(TxRx)(μs) = Duration of TxRxturnaround time in μs 2 μs (1.2.17) T_(IFS)(μs) = Duration of interframespace (guard time interval) in 226 μs (1.2.18) μs, inclusive of T_(TxRx)T_(TDD)(μs) = Duration of OFDM cyclic prefix (guard interval) μs 2,500μs (1.2.19) f_(sub)(MHz) = Subcarriers spacing in MHz (OFDM LPHY) 0.3125MHz (1.2.20) W_(active)(MHz) = Active bandwith of signal in MHz 16.25MHz (1.2.21) Node-address dependent datalink parameters: N_(ant) =Antennas available at node Variable (1.2.22) M_(ant) = Antennas used atnode (≦N_(ant)) Variable (1.2.23) N_(port) = Physical ports supportableat node (≦N_(ant)) Variable (1.2.24) M_(port) = Physical ports used atnode (≦N_(port)) Variable (1.2.25) Link-address dependent datalinkparameters: M_(codec) = Codec rate employed on link (0 

 no data Variable (1.2.26) transported) M_(bit) = Bits/frame Tx″d overthe link (M_(bit)* M_(codec)), 0 

Variable (1.2.27) none Range Node-address independent datalink indices:n_(FHT) = Physical FHT input bin index 1: N_(FHT) (1.2.28) m_(FHT) =Logical FHT input bin index 1: M_(FHT) (1.2.29) m_(data) = Logical databin index 1: M_(data) (1.2.30) m_(embed) = Logical adaptation bin index1: M_(embed) (1.2.31) n_(sub) = Physical subcarrier index 1: N_(sub)(1.2.32) m_(sub) = Logical subcarrier index 1: M_(sub) (1.2.33) m_(QAM)= Logical QAM data index 1: M_(QAM) (1.2.34) n_(OFDM) = Physical OFDMsymbol index 1: N_(OFDM) (1.2.35) m_(OFDM) = Logical OFDM symbol index1: M_(OFDM) (1.2.36) n_(TDD) = TDD subframe index (TDD instantiations)1: N_(TDD) (1.2.37) n_(frame) Data frame index (ignores TDD framing) —(1.2.38) Node-address dependent datalink indices: n_(ant) = Physicalantenna index 1: N_(ant) (1.2.39) m_(ant) = Logical antenna index 1:M_(ant) (1.2.40) n_(port) = Physical port index 1: N_(port) (1.2.41)m_(port) = Logical port index 1: M_(port) (1.2.42) m_(frame) = Logicalframe index, shared by consecutive node ≧0 (1.2.43) receive and transmitframes Link-address dependent datalink indices: m_(bit) = Logical databit (codes input) index 1: M_(bit) (1.2.44) Datalink mappings:v_(embed)(m_(embed)) = Physical Hadamard bin modulated by logicaltransmit pilot (1.2.45) bin m_(embed) v_(data)(m_(data)) = PhysicalHadamard bin modulated by logical data bin m_(data) (1.2.46)v_(sub)(m_(sub)) = Physical subcarrier modulated by logical subcarrierm_(sub) (1.2.47) f_(sub)(m_(sub)) = Physical baseband link frequency oflogical subcarrier m_(sub) (1.2.48) v_(frame)(m_(frame), m_(TDD)) =Physical frame carrying PPDU with frame index m_(frame), TDD (1.2.49)subframe index m_(frame). μ_(port)(m_(LA)) = Logical transmit or receiveport (as appropriate) (1.2.50) providing data for link address m_(LA).By convention, the same logical port is used on the return path,m_(port)(m_(LA)) = v_(port)(m_(RLA)), m_(RLA) = μ_(RLA)(m_(LA)).μ_(LA)(m_(port)) = LA serviced by port m_(port). Inverse ofμ_(port)(m_(LA)). (1.2.51)

Data and Parameter Arrays:

Dimensions Transmit data arrays: B_(TNA)(m_(frame)) = Transmitted bitstransmitted, frame m_(frame), M_(bit)xM_(port) (1.3.1)B_(TNA)(m_(frame)) = [b_(TNA)(1; m_(frame)) . . . b_(TNA)(M_(port);m_(frame))], b_(TNA)(m_(port); m_(frame)) = bits Tx'd from node m_(TNA)over port m_(port)   m_(port) = μ_(port)(m_(LA)), where μ_(TNA)(m_(LA))= m_(TNA) Q_(TNA)(m_(sub), m_(frame)) = QAM data transmitted, subcarrierm_(sub), frame m_(frame), M_(data)xM_(port) (1.3.2)${Q_{TNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{q_{TNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{q_{TNA}^{H}\left( {{M_{data};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ q_(TNA)(m_(data); m_(sub), m_(frame)) = QAM data Tx'd ondata bin m_(data) D_(TNA)(m_(sub), m_(frame)) = FHT input data,subcarrier m_(sub), frame m_(frame), M_(OFDM)xM_(port) (1.3.3)${D_{TNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{d_{TNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{d_{TNA}^{H}\left( {{M_{FHT};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ C_(TNA)(m_(sub), m_(frame)) = TRANSEC-scrambled FHToutput data, subcarrier M_(OFDM)xM_(port) (1.3.4) m_(sub), framem_(frame),${C_{TNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{c_{TNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{c_{TNA}^{H}\left( {{M_{OFDM};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ S_(TNA)(m_(sub), m_(frame)) = OFDM modulator input data,subcarrier m_(sub), frame M_(OFDM)xM_(ant) (1.3.5) m_(frame),${S_{TNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{s_{TNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{s_{TNA}^{H}\left( {{M_{OFDM};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ Transmit parameter arrays: R_(RNA)(m_(sub), m_(frame)) =RNA TRANSEC code, subcarrier m_(sub), frame m_(frame); M_(OFDM)xM_(port)(1.3.6) row m_(port) = receive code for node m_(RNA) = μ_(RNA)(μ_(LA)(m_(port))) r_(RNA)(m_(port); m_(sub), m_(frame)) = r(m_(sub),m_(frame); μ_(RNA)(μ_(LA)(m_(port)))), G_(TNA)(m_(sub), m_(frame)) = TNAdistribution weights, subcarrier m_(sub), frame m_(frame).M_(ant)xM_(port) (1.3.7) γ_(RLA)(m_(frame)) = Target return-link SINR's,frame m_(frame). 1xM_(port) (1.3.8) h_(Tx)(m_(sub)) = Transmitsubcarrier mask (OFDM LPHY), M_(ant)x1 (1.3.9)${h_{TX}\left( m_{sub} \right)} = {\left( \frac{{{\pi f}_{sub}\left( m_{sub} \right)}T_{DAC}}{\sin \left( {{{\pi f}_{sub}\left( m_{sub} \right)}T_{DAC}} \right)} \right){h_{TRC}\left( m_{sub} \right)}}$where T_(DAC) = 1/f_(DAC) is the (node-specific) DAC output sampleperiod. h_(TRC)(m_(sub)) = Transmit-receive compensation weights,equalizes M_(ant)x1 (1.3.10) path differences between the RF switch andthe DAC (transmit path) and ADC (receive path) at each node in thenetwork. Computed during scheduled Transmit/receive compensation events.Receive data arrays: X_(RNA)(m_(sub), m_(frame)) = OFDM demod outputdata, subcarrier m_(sub), frame M_(OFDM)xM_(ant) (1.3.11) m_(frame).Y_(RNA)(m_(sub), m_(frame)) = TRANSEC-descrambled FHT output data,subcarrier M_(FHT)xM_(ant) (1.3.12) m_(sub), frame m_(frame).${Y_{RNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{y_{RNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{y_{RNA}^{H}\left( {{M_{OFDM};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ P_(RNA)(m_(sub), m_(frame)) = Deembeded pilot data,subcarrier m_(sub), frame m_(frame). M_(embed)xM_(ant) (1.3.13)${P_{RNA}\left( {m_{sub},m_{frame}} \right)} = \begin{bmatrix}{p_{RNA}^{H}\left( {{1;m_{sub}},m_{frame}} \right)} \\\vdots \\{p_{RNA}^{H}\left( {{M_{embed};m_{sub}},m_{frame}} \right)}\end{bmatrix}$ Z_(RNA)(m_(sub), m_(frame)) = Deembeded QAM data,subcarrier m_(sub), frame m_(frame). M_(data)xM_(ant) (1.3.14)Q_(RNA)(m_(sub), m_(frame)) = Demodulated QAM data, subcarrier m_(sub),frame m_(frame). M_(data)xM_(port) (1.3.15) B_(RNA)(m_(frame)) = Decodedbits, frame m_(frame) M_(bit)xM_(port) (1.3.16) Receive parameterarrays: r_(RNA)(m_(sub), m_(frame)) = NA TRANSEC code, subcarrierm_(sub), frame m_(frame). M_(OFDM)x1 (1.3.17) Used at node m_(RNA)during receive operations, and at nodes attempting to communicate withnode m_(RNA) during their transmit operations. W_(RNA)(m_(sub),m_(frame)) = Rx combiner weights, subcarrier m_(sub), frame m_(frame).M_(ant)xM_(port) (1.3.18) A_(Rx)(m_(sub), m_(frame)) = Rx spatialsignature estimates, subcarrier m_(sub), frame M_(ant)xM_(port) (1.3.19)m_(frame). γ_(LA)(m_(sub), m_(frame)) = Estimated link SINR's,subcarrier m_(sub), frame m_(frame). 1xM_(port) (1.3.20) Conceptualparameter arrays (not generated, but referred to in operations):t_(TNA)(m_(frame)) = [Sparse] NA transmit pilot, subcarrier m_(sub),frame M_(embed)xl (1.3.21) m_(frame). t_(TNA)(m_(frame)) = {square rootover (M_(embed))}e(k_(TNA)(m_(frame))) C_(FHT) = Unitary Walshtransformation matrix M_(FHT)xM_(FHT) (1.3.22) S_(data) = Shift matrix,maps logical data bins to FHT input bins M_(FHT)xM_(data) (1.3.23)S_(pilot) = Shift matrix, maps logical pilot bins to FHT input binsM_(FHT)xM_(pilot) (1.3.24) Shift matrix, maps logical bins to physicalFHT input M_(FHT)xM_(FHT) (1.3.25) S_(FHT) = bins,   S_(FHT) =[S_(pilot) S_(data)]

Upper-PHY Signal Processing Operations: Transmit Operations

Starting with the transmit bits B_(TNA)(m_(frame)) to be transmittedover logical subcarrier m_(sub) and logical frame m_(frame), perform thefollowing operations.

-   Step TP1: Separately encode each row of transmitted bits    B_(TNA)(m_(frame)) into QAM symbols, and map to subcarriers to form    QAM transmit data Q_(TNA)(m_(sub),m_(frame)). The bit-to-QAM encoder    is not specified here. However, the default encoder will be    operations cited in the 802.11a standards specification.-   Step TP2: Embed the transmit pilot, and map pilot & data to FHT    input bins

$\begin{matrix}\begin{matrix}{{D_{TNA}\left( {m_{{sub},}m_{frame}} \right)} = {S_{FHT}\begin{bmatrix}{{t_{TNA}\left( m_{frame} \right)}1_{M_{port}}^{T}} \\{Q_{TNA}\left( {m_{sub},m_{frame}} \right)}\end{bmatrix}}} \\{= {{S_{pilot}\left( {{t_{TNA}\left( m_{frame} \right)}1_{M_{port}}^{T}} \right)} +}} \\{{S_{data}{Q_{Tx}\left( {m_{sub},m_{frame}} \right)}}}\end{matrix} & \begin{matrix}\left( {2.1{.1}} \right) \\\; \\\left( {2.1{.2}} \right)\end{matrix}\end{matrix}$

-   Step TP3: Embed receive pilot for RNA's communicating with the node.

C _(TNA)(m _(sub) ,m _(frame))=R _(RNA)(m _(sub) ,m _(frame)).*(C _(FHT)D _(TNA)(m _(sub) ,m _(frame)))  (2.1.3)

-   Step TP4: Distribute the embedded data over the output antennas.

S _(TNA)(m _(sub) ,m _(frame))=(1_(M) _(OFDM) h _(Tx) ^(T)(m_(sub))).*(C _(TNA)(m _(sub) ,m _(frame))G _(TNA) ^(R)(m _(sub) ,m_(frame))),  (2.1.4)

where “.*” denotes the element-wise multiply operation, and 1_(M) is theMx1 all-ones vector.

Two algorithms are specified here to adapt transmit distribution weights{G_(TNA)(m_(sub),m_(frame))},

-   -   A retrodirective max-SINR approach that sets        {G_(TNA)(m_(sub),m_(frame))} proportional to the receive weights        that maximize signal-to-interference-and-noise ratio (SINR) on        the return path, and    -   A retrodirective max-SIR approach that sets        {G_(TNA)(m_(sub),m_(frame))} proportional to the receive weights        that maximize signal-to-interference ratio (SIR), i.e., that        provide hard transmit nulls, on the return path.        The max-SIR approach is recommended for initialization of new        links; the max-SINR approach is recommended for steady state        operation and tracking The max-SINR transmit weight adaptation        algorithm is described in Section 3.2. The max-SIR transmit        weight adaptation algorithm is described in Section 4.2.

Receive Processing Operations

Starting with the multiantenna data X_(RNA)(m_(sub),m_(frame)) receivedand OFDM-demodulated over logical subcarrier m_(sub) and logical framem_(frame), perform the following operations.Step RP1: Remove the receive pilot, and inverse-FHT descrambled data

Y _(RNA)(m _(sub) ,m _(frame))=C _(FHT) ^(H)((r _(RNA)*(m _(sub) ,m_(frame))1_(M) _(ant) _((m) _(RNA) ₎ ^(T)).*X _(RNA)(m _(sub) ,m_(frame)))  (2.2.1)

Step RP2: Separate pilot & data components

P _(RNA)(m _(sub) ,m _(frame))=S _(pilot) ^(T) Y _(RNA)(m _(sub) ,m_(frame))  (2.2.2)

X _(RNA)(m _(sub) ,m _(frame))=S _(data) ^(T) Y _(RNA)(m _(sub) ,m_(frame))  (2.2.3)

Steps RA, Detect transmit pilots and estimate their SINR'sγ_(LA)(m_(sub),m_(frame)) (Sections 3.1, 4.1).

TA:

-   -   Compute combiner weights {W_(RNA)(m_(sub),m_(frame))} (Sections        3.1, 4.1).    -   Compute distribution weights {G_(TNA)(m_(sub),m_(frame))} to be        used on the return path (Sections 3.2, 4.2).        Step RP3: Recover the QAM link data:

Q _(RNA)(m _(sub) ,m _(frame))=Z _(RNA)(m _(sub) ,m _(frame))W _(RNA)(m_(sub) ,m _(frame))  (2.2.4)

Two algorithms are specified here to adapt receive combiner weights{W_(RNA)(m_(sub),m_(frame))},

-   -   A retrodirective max-SINR approach that adapts        {W_(RNA)(m_(sub),m_(frame))} to maximize        signal-to-interference-and-noise ratio (SINR) of the received        pilot data, and    -   A retrodirective max-SIR approach that adapts        {W_(RNA)(m_(sub),m_(frame))} to maximize signal-to-interference        ratio (SIR) of the received pilot data, i.e., that provide hard        receive nulls to separate the signals of interest to the node.        The max-SIR approach is recommended for initialization of new        links; the max-SINR approach is recommended for steady state        operation and tracking The max-SINR transmit weight adaptation        algorithm is described in Section 3.2. The max-SIR transmit        weight adaptation algorithm is described in Section 4.2.

Max-SINR Adaptation Algorithm Adaptive Receive Algorithm

Starting with the multiantenna received and deembedded pilot data(referred to as P_(RNA)(m_(sub)) or P_(RNA) as appropriate to simplifyarguments) received and OFDM-demodulated over logical subcarrier m_(sub)and logical frame m_(frame), perform the following operationsStep RA1: Compute QRD of P_(RNA)(m_(sub),m_(frame))

{Q(m _(sub)),R(m _(sub))}=QRD{P _(RNA)(m _(sub) ,m _(frame))}, suchthat  (3.1.1)

$\begin{matrix}\begin{matrix}{\left. {R\left( m_{sub} \right)} \right\} = {{chol}\left\{ {{P_{RNA}^{H}\left( {m_{sub},m_{frame}} \right)}{P_{RNA}\left( {m_{sub},m_{frame}} \right)}} \right\}}} \\{= {{chol}\left\{ {P_{RNA}^{H}{P_{RNA}\left( {m_{sub},m_{frame}} \right)}} \right\}}}\end{matrix} & \begin{matrix}\left( {3.1{.2}} \right) \\\left( {3.1{.3}} \right)\end{matrix}\end{matrix}$C(m _(sub))=R ⁻¹(m _(sub))  (3.1.4)

$\begin{matrix}\begin{matrix}{{{Q\left( m_{sub} \right)} = {{P_{RNA}\left( {m_{sub},m_{frame}} \right)}{C\left( m_{sub} \right)}}},} \\{\left( {{{Q^{H}\left( m_{sub} \right)}{Q\left( m_{sub} \right)}} = I_{M_{ant}}} \right)} \\{= \begin{bmatrix}{q^{H}\left( {1;m_{sub}} \right)} \\\vdots \\{q^{H}\left( {M_{embed};m_{sub}} \right)}\end{bmatrix}}\end{matrix} & \begin{matrix}\left( {3.1{.5}} \right) \\\; \\\left( {3.1{.6}} \right)\end{matrix}\end{matrix}$

Step RA2: Detect candidate TNA transmit pilots and spatially whitenedadaptation weights

η(m _(embed) ;m _(sub))=∥q(m _(embed) ;m _(sub))∥²  (3.1.7)

$\begin{matrix}{\mspace{79mu} {{\gamma \left( {m_{{embed};}m_{sub}} \right)} = \frac{\eta \left( {m_{embed};m_{sub}} \right)}{1 - {\eta \left( {m_{embed};m_{sub}} \right)}}}} & \left( {3.1{.8}} \right) \\{\mspace{79mu} {{c_{\det}\left( m_{embed} \right)} = {\frac{1}{M_{sub}}{\sum\limits_{m_{sub} = 1}^{M_{sub}}{\log_{2}\left( {1 + {\gamma \left( {m_{embed};m_{sub}} \right)}} \right)}}}}} & \left( {3.1{.9}} \right) \\{\left\{ {m_{embed}(m)} \right\}_{m = 1}^{M_{\det}} = {{m_{embed}(m)}\mspace{14mu} {satisfying}\left\{ \begin{matrix}{{{c_{\det}\left( {m_{embed}(m)} \right)} \geq c_{thresh}},{and}} \\{{c_{\det}\left( {m_{embed}(m)} \right)} \geq {c_{\det}\left( {m_{embed}\left( {m + 1} \right)} \right)}}\end{matrix} \right.}} & \left( {3.1{.10}} \right)\end{matrix}$U _(det)(m _(sub))=√{square root over (M _(embed))}[q(m _(embed)(1);m_(sub)) . . . q(m _(embed)(m _(det));m _(sub))]  (3.1.11)

η_(det)(m _(sub))=[η(m _(embed)(1);m _(sub)) . . . η(m _(embed)(m_(det));m _(sub))]  (3.1.12)

γ_(det)(m _(sub))=[γ(m _(embed)(1);m _(sub)) . . . γ(m _(embed)(m_(det));m _(sub))]  (3.1.13)

Step RA3: If spatial signature estimates A_(port)(m_(sub)) areavailable, where A_(port)(m_(sub)) is the import column ofA_(RNA)(m_(sub)) (see Step RA6), associate detected transmit pilots withreceive ports and link addresses.

U _(port)(m _(det) ,m _(sub))=C ^(H)(m _(sub))a _(port)(m _(port) ;m_(sub))  (3.1.14)

$\begin{matrix}{{\rho \left( {m_{\det},{m_{port};m_{sub}}} \right)} = {\frac{{{{u_{\det}^{H}\left( {m_{\det};m_{sub}} \right)}{u_{port}\left( {m_{port};m_{sub}} \right)}}}^{2}}{M_{embed};{{\eta_{\det}\left( {m_{\det};m_{sub}} \right)}{{u_{port}\left( {m_{port};m_{sub}} \right)}}^{2}}} \leq 1}} & \left( {3.1{.15}} \right) \\{{c_{match}\left( {m_{\det},m_{port}} \right)} = {\frac{1}{M_{sub}}{\sum\limits_{m_{sub} = 1}^{M_{sub}}{\log_{2}\left( {1 + {{\gamma \left( {m_{\det};m_{sub}} \right)}{\rho \left( {m_{\det},{m_{port};m_{sub}}} \right)}}} \right)}}}} & \left( {3.1{.16}} \right) \\{\mspace{79mu} {\left\{ {m_{\det}\left( m_{port} \right)} \right\}_{m_{port} = 1}^{M_{port}} = {\arg \; {\max\limits_{m_{\det}}\left\{ {c_{match}\left( {m_{\det};m_{port}} \right)} \right\}}}}} & \left( {3.1{.17}} \right)\end{matrix}$

Step RA4: Drop and add ports, based on the port matching statisticc_(match)(m_(det),m_(port)).

$\begin{matrix}{\left\{ m_{drop} \right\}_{1}^{M_{drop}} = {\arg \; {\max\limits_{m_{port}}\left\{ {{\max\limits_{m_{\det}}\left\{ {c_{match}\left( {m_{\det};m_{port}} \right)} \right\}} < c_{thresh}} \right\}}}} & \left( {3.1{.18}} \right)\end{matrix}${m _(add)}₁ ^(M) ^(add) =m _(det) ∉{m _(det)(m _(port))}_(m) _(port=1)^(M) ^(port)   (3.1.19)

M _(port) ←M _(port) −M _(drop) +M _(add)  (3.1.20)

{m _(det)(m _(port))}_(m) _(port=1) ^(M) ^(port) ←({m _(det)(m_(port))}\{m _(det)(m _(drop))})∪{m _(add)}  (3.1.21)

Step RA5: Assign receive ports and link statistics η_(LA)(m_(sub)) andγ_(LA)(m_(sub)).

η_(LA)(m _(port) ;m _(sub))=η_(det)(m _(det)(m _(port));m_(sub))  (3.1.22)

γ_(LA)(m _(port) ;m _(sub))=γ_(det)(m _(det)(m _(port));m_(sub))  (3.1.23)

u(m _(port) ;m _(sub))=u _(det)(m _(det)(m _(port));m _(sub))  (3.1.24)

U(m _(sub))=[u(1;m _(sub)) . . . u(M _(port) ;m _(sub))]  (3.1.25)

Step RA6: Estimate spatial steering matrices{A_(RNA)(m_(sub),m_(frame))}.

A _(RNA)(m _(sub) ,m _(frame))=C ^(H)(m _(sub))U(m _(sub))  (3.1.26)

Step RA7: Compute combiner weights {W_(RNA)(m_(sub),m_(frame))}.

W _(RNA)(m _(sub) ,m _(frame))=C(m _(sub))U(m _(sub))  (3.1.27)

Adaptive Transmit Algorithm

Starting with the receive weights W_(RNA)(m_(sub),m_(frame)) given in(3.1.27) and target SINR's γ_(RLA) for the return link, perform thefollowing operations.

Step TA1: Scale whitened transmit weights,

$\begin{matrix}{{{\eta_{RLA}\left( m_{port} \right)} = \frac{\gamma_{RLA}\left( m_{RLA} \right)}{1 + {\gamma_{RLA}\left( m_{RLA} \right)}}},{m_{port} = {{\mu_{port}\left( m_{RLA} \right)} = {\mu_{port}\left( m_{RLA} \right)}}}} & \left( {3.2{.1}} \right) \\{\left. {\pi_{RLA}\left( {m_{port};m_{sub}} \right)}\leftarrow\frac{\eta_{RLA}\left( m_{port} \right)}{\eta_{LA}\left( {m_{port};m_{sub}} \right)} \right.,\left( {{\eta_{LA}\left( {m_{port};m_{sub}} \right)}\mspace{20mu} {given}\mspace{14mu} {in}\mspace{14mu} \left( {3.1{.22}} \right)} \right)} & \left( {3.2{.2}} \right)\end{matrix}$

Step TA2: Compute distribution weights {G_(TNA)(m_(sub),m_(frame))}.

G _(TNA)(m _(sub) ,m _(frame))[√{square root over (π(1,m _(sub)))}w_(RNA)(1,m _(sub)) . . . √{square root over (π(M _(port) ,m _(sub)))}w_(RNA)(M _(port) ,m _(sub))]  (3.2.3)

The target SINR's can also be derived from rate targets based onperformance of the codec's used in the system, or from capacity targets{C_(RLA)(m_(RLA))} or spectral efficiency targets {c_(RLA)(m_(RLA))},via the formula

c _(RLA)(m _(RLA))=1.63C _(RLA)(m _(RLA))/W _(active)  (3.2.4)

γ_(RLA)(m _(RLA))=λ_(gap)(2^(c) ^(RLA) ^((m) ^(RLA) ⁾−1)c _(RLA)(m_(RLA))  (3.2.5)

Where 1.63=1/0.6144 is the inverse efficiency of the airlink, whichincludes overhead for transmit pilots (0.9375 efficiency), the OFDMcyclic prefix (0.80 efficiency) and TDD framing (0.8192 efficiency), andwhere λ_(gap) is the SNR coding gap of the QAM codec. Target SINR, rate,or capacity can be set at either end of the link, i.e., as a transmitterdesign goal or as a control parameter passed from the link or network.In the first two cases, this adaptation is referred to here as locallyenabled network optimization (LEGO).

Note that steps (3.2.1)-(3.2.3) adjust output power to meet a link SINR(or link rate or capacity) criterion. That is, the system will attemptto adjust transmit power at each node to meet this criterion. Also notethat steps (3.2.1)-(3.2.3) require no information from other links inthe network, including links originating from the same node. While thiscan be a highly desirable attribute in many applications, it has somedrawbacks in practice. In particular, if the target SINR's are set toohigh, the resultant network can fail to converge and drive itstransmitters into saturation. This event can be detected by computingthe conducted power into each antenna,

$\begin{matrix}{{P_{RLA}\left( m_{ant} \right)} = {M_{sub}{\sum\limits_{m_{port} = 1}^{M_{port}}{{g\left( {m_{ant},m_{port}} \right)}}^{2}}}} & \left( {3.2{.6}} \right)\end{matrix}$

and monitoring P_(RLA)(m_(ant)) to ensure compliance with conductedpower requirements.

In addition, the convergence time of the LEGO algorithm can be slow,especially during initial acquisition of multiple links. Thisperformance can be improved by employing a max-SIR algorithm that formshard nulls during the initial link acquisition period. This algorithm iseasily implemented as an extension of the max-SINR algorithm describedabove.

Max-SIR Adaptation Algorithm Adaptive Receive Algorithm

Starting with the multiantenna received and deembedded pilot dataX_(Px)(m_(sub),m_(frame)) (referred to as X_(Px)(m_(sub)) or X_(Px) asappropriate to simplify arguments) received and OFDM-demodulated overlogical subcarrier m_(sub) and logical frame m_(frame), perform thefollowing operations.

-   Step RA1: Compute QRD of P_(RNA)(m_(sub),m_(frame)), using    (3.1.1)-(3.1.6).-   Step RA2: Detect candidate TNA transmit pilots and    spatially-whitened max-SINR adaptation weights using    (3.1.7)-(3.1.13).-   Step RA3: If spatial signature estimates A_(RNA)(m_(sub)) are    available (see Step RA7), associate detected transmit pilots with    receive ports and link addresses, using (3.1.14)-(3.1.17).-   Step RA4: Drop and add ports using (3.1.18)-(3.1.21).-   Step RA5: Assign max-SINR whitened transmit/receive weights and link    statistics using (3.1.22)-(3.1.25).-   Step RA6: Estimate spatial steering matrices    {A_(RNA)(m_(sub),m_(frame))} using 3.1.26.-   Step RA7: Compute combiner weights {W_(RNA)(m_(sub),m_(frame))},    using (3.1.27).-   Step RA7.1: Compute null-steering receive weights and SINR's

C _(⊥)(m _(sub))=(U ^(H)(m _(sub))U(m _(sub)))⁻¹  (4.1.1)

η_(LA)(m _(posrt) ,m _(sub))←1/diag{C _(⊥)(m _(sub))} (replaces η_(LA)(m_(posrt) ,m _(sub)) provided by (3.1.22))  (4.1.2)

$\begin{matrix}{\left. {\gamma_{LA}\left( {m_{posrt},m_{sub}} \right)}\leftarrow\frac{\eta_{LA}\left( {m_{port};m_{sub}} \right)}{1 - {\eta_{LA}\left( {m_{port};m_{sub}} \right)}} \right.\left( {{replaces}\mspace{14mu} {\gamma_{LA}\left( {m_{port},m_{sub}} \right)}\mspace{14mu} {provided}\mspace{14mu} {by}\mspace{14mu} \left( {3.1{.22}} \right)} \right)} & \left( {4.1{.3}} \right)\end{matrix}$

w _(RNA)(m _(sub) ,m _(frame))←w _(RNA)(m _(sub) ,m _(sub))C _(⊥)(m_(sub))  (4.1.4)

Adaptive Transmit Algorithm

Starting with the transmit weights w_(RNA)(m_(sub),m_(frame)) given in(4.1.4) and target SINR's γ_(RLA) for the return link, perform thefollowing operations.

Step TA1: Compute η_(RLA)(m_(port)) using (3.2.1).Step Compute power scaling π(m_(port); m_(sub)) using

TA1.1:

π(m _(port) ;m _(sub))=η_(RLA)(m _(port))η_(LA)(m _(port) ;m _(sub)),(η_(LA)(m _(port) ;m _(sub)) given in (3.1.22))  (4.2.1)

Step TA2: Compute combiner weights {G_(RNA)(m_(sub),m_(frame))} using3.2.3The target SINR's can also be derived from rate targets based onperformance of the codec's used in the system, or from capacity targets{C_(RLA)(m_(RLA))} or spectral efficiency targets {c_(RLA)(m_(RLA))},using (3.2.4)-(3.2.5), and conducted power can be monitored using(3.2.6). In addition, the SINR and SIR estimates given by (3.1.23 and(4.1.3) can be used to detect “overloaded network” conditions where themax-SIR solution is misadjusting significantly from the max-SINR result.While this invention has been described with reference to one or moreillustrative embodiments, this description is not to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, differing order of the sub-steps (includingparallel and partial processing for one or more operations thereof), aswell as other embodiments of the invention will be apparent to thoseskilled in the art upon referencing this disclosure. It is thereforeintended this disclosure encompass any combination of the specificsdescribed here and such modifications or embodiments. Furthermore, thescope of this invention includes any combination of the subordinateparts from the different embodiments disclosed in this specification,and is not limited to the specifics of the preferred embodiment or anyof the alternative embodiments mentioned above. Individualconfigurations and embodiments of this invention may contain all, orless than all, of those disclosed in the specification according to theneeds and desires of that user. The claims stated herein should furtherbe read as including those elements which are not necessary to theinvention yet are in the prior art, particularly that referenced andincorporated herein thereby, and are necessary to the overall functionof that particular claim, and should be read as including, to themaximum extent permissible by law, known functional equivalents to thespecification's disclosure, even though those functional equivalents arenot exhaustively detailed herein or individually claimed below due tothe legal preferences for limiting the number of claims and the law'sintent to negate any requirement for combinatorial explosion foroverly-detailed description and claiming of known and foreseeablealternatives.

Although the present invention has been described chiefly in terms ofthe presently preferred embodiment, it is to be understood that thedisclosure is not to be interpreted as limiting. Various alterations andmodifications will no doubt become apparent to those skilled in the artafter having read the above disclosure. Such modifications may involveother features which are already known in the design, manufacture anduse of adaptive and transitional MIMO systems, both hardware andassociated software therefore, and which may be used instead of or inaddition to features already described herein. The examples herein arenot limiting but instructive of the embodiment of the invention, andvariations which are readily derived through programming or embeddedhardware transformations which are standard or known to the appropriateart are not excluded by omission. Accordingly, it is intended that theappended claims are interpreted as covering all alterations andmodifications as fall within the true spirit and scope of the inventionin light of the prior art.

Additionally, although claims have been formulated in this applicationto particular combinations of elements, it should be understood that thescope of the disclosure of the present application also includes anysingle novel element or any novel combination of elements disclosedherein, either explicitly or implicitly, whether or not it relates tothe same invention as presently claimed in any claim and whether or notit mitigates any or all of the same technical problems as does thepresent invention. The applicants hereby give notice that new claims maybe formulated to such features and/or combinations of such featuresduring the prosecution of the present application or of any furtherapplication derived there from.

1. An apparatus, comprising: a spatially diverse antennae array of Mantennae, where M is greater than or equal to two; at least onemultiple-input and multiple-output/orthogonal frequency divisionmultiplexing-capable transceiver in communication with each antenna inthe spatially diverse antennae array of M antennae; encoding circuitrycapable of causing first data to be encoded; decoding circuitry capableof causing second data to be decoded; and processing circuitry capableof causing diversity combining, the processing circuitry incommunication with the multiple-input and multiple-output/orthogonalfrequency division multiplexing-capable transceiver, the encodingcircuitry, and the decoding circuitry, the processing circuitry capableof causing the apparatus to: receive at least two first diverse signals,combine at least two of the at least two first diverse signals, generateat least two second diverse signals based on at least one aspect of theat least two first diverse signals, and simultaneously transmit the atleast two second diverse signals; wherein the apparatus is configuredsuch that at least one of the at least two second diverse signals iscapable of being received by a multiple-input andmultiple-output-capable node.
 2. The apparatus of claim 1, wherein theapparatus is configured such that M is equal to
 2. 3. The apparatus ofclaim 1, wherein the apparatus is configured such that M is equal to 4.4. The apparatus of claim 1, wherein the apparatus is configured suchthat a first set of the spatially diverse antennae array of M antennaeare capable of receiving the at least two first diverse signals.
 5. Theapparatus of claim 4, wherein the apparatus is configured such that asecond set of the spatially diverse antennae array of M antennae arecapable of simultaneously transmitting the at least two second diversesignals.
 6. The apparatus of claim 5, wherein the apparatus isconfigured such that the first set of the spatially diverse antennaearray of M antennae capable of receiving the at least two first diversesignals is distinct from the second set of the spatially diverseantennae array of M antennae capable of simultaneously transmitting theat least two second diverse signals.
 7. The apparatus of claim 6,wherein the apparatus is configured such that the first set of thespatially diverse antennae array of M antennae capable of receiving theat least two first diverse signals is the same as the second set of thespatially diverse antennae array of M antennae capable of simultaneouslytransmitting the at least two second diverse signals.
 8. The apparatusof claim 1, wherein the apparatus is configured such that the spatiallydiverse antennae array of M antennae is circularly symmetric.
 9. Theapparatus of claim 1, wherein the apparatus is configured such that theprocessing circuitry includes digital signal processing circuitrycapable of performing digital signal processing to convert analog radiosignals into digital signals and digital signals into analog radiosignals.
 10. The apparatus of claim 1, wherein the apparatus isconfigured such that the at least one multiple-input andmultiple-output/orthogonal frequency division multiplexing-capabletransceiver includes a multitone transmission element.
 11. The apparatusof claim 10, wherein the apparatus is configured such that the at leastone multiple-input and multiple-output/orthogonal frequency divisionmultiplexing-capable transceiver including the multitone transmissionelement is capable of multitone transmitting a first multitone formatcapable of being used by a first transceiver that is different than asecond multitone format capable of being used by a second transceiver.12. The apparatus of claim 1, wherein the apparatus is configured suchthat the processing circuitry includes a transceiver controller.
 13. Theapparatus of claim 1, wherein the apparatus is configured such that theat least one multiple-input and multiple-output/orthogonal frequencydivision multiplexing-capable transceiver includes at least oneFixed-Fourier Transform enabling chip.
 14. The apparatus of claim 13,wherein the apparatus is configured such that the at least onemultiple-input and multiple-output/orthogonal frequency divisionmultiplexing-capable transceiver is capable of implementing orthogonalfrequency division multiplexing utilizing the at least one Fixed-FourierTransform enabling chip.
 15. The apparatus of claim 1, wherein theapparatus is configured such that the at least one multiple-input andmultiple-output/orthogonal frequency division multiplexing-capabletransceiver is a vector orthogonal frequency division multiplexingtransceiver.
 16. The apparatus of claim 15, wherein the apparatus isconfigured such that the vector orthogonal frequency divisionmultiplexing transceiver is capable of linearly combining data receivedover each antenna in the spatially diverse antennae array of M antennae.17. The apparatus of claim 1, wherein the apparatus is configured suchthat the encoding circuitry is capable of incorporating QAM or PSKsymbols prior to transmission.
 18. The apparatus of claim 1, wherein theapparatus is configured such that the decoding circuitry is capable ofinterpreting QAM or PSK symbols.
 19. The apparatus of claim 1, whereinthe apparatus is configured such that the encoding circuitry is capableof performing trellis encoding.
 20. The apparatus of claim 1, whereinthe apparatus is configured such that the at least one of the at leasttwo second diverse signals is capable of being received by a nodelocated in a point to multipoint network.
 21. The apparatus of claim 1,wherein the apparatus is configured such that the at least one of the atleast two second diverse signals is capable of being received by a nodelocated in a mesh network.
 22. The apparatus of claim 21, wherein theapparatus is further configured such that the node located in the meshnetwork includes a non-multiple-input and non-multiple-output capablenode.
 23. The apparatus of claim 21, wherein the apparatus is configuredsuch that the at least one of the at least two second diverse signals iscapable of being received by a node located in a star network.
 24. Theapparatus of claim 1, wherein the apparatus is configured such that theat least two second diverse signals are capable of being simultaneouslytransmitted as frequency coincident communications.
 25. The apparatus ofclaim 1, wherein the apparatus is configured such that the at least twosecond diverse signals are capable of being transmitted as time andfrequency coincident communications.
 26. The apparatus of claim 1,wherein the apparatus is configured such that generating the at leasttwo second diverse signals includes generating spatially diversesignals.
 27. The apparatus of claim 26, wherein the apparatus isconfigured such that generating the spatially diverse signals includestransmitting the spatially diverse signals from the spatially diverseantennae array of M antennae.
 28. The apparatus of claim 1, wherein theapparatus is configured such that generating the at least two seconddiverse signals includes generating polarization diverse signals. 29.The apparatus of claim 28, wherein the apparatus is configured such thatgenerating the polarization diverse signals includes transmitting thespatially diverse signals from a polarization diverse antennae array.30. The apparatus of claim 1, wherein the apparatus is configured suchthat generating the at least two second diverse signals includesgenerating route diverse signals.
 31. The apparatus of claim 30, whereinthe apparatus is configured such that generating the route diversesignals includes transmitting the spatially diverse signals to a node ina location separate than a location of the apparatus.
 32. The apparatusof claim 1, wherein the apparatus is configured such that simultaneouslytransmitting the at least two second diverse signals includestransmitting such that at least two of the at least two second diversesignals are capable of being received by a multiple-input andmultiple-output capable node.
 33. The apparatus of claim 1, wherein theapparatus is configured such that simultaneously transmitting the atleast two second diverse signals includes transmitting such that atleast two of the at least two second diverse signals are capable ofbeing received by a non-multiple-input and non-multiple-output capablenode.
 34. The apparatus of claim 1, wherein the apparatus is configuredto perform transmit beamforming.
 35. The apparatus of claim 34, whereinthe apparatus is configured such that the transmit beamforming includesconstructing linear distribution weights.
 36. The apparatus of claim 1,wherein the apparatus is configured such that linear combiner weightsobtained during a receive operation are capable of being used toconstruct linear distribution weights for a subsequent transmitoperation.
 37. The apparatus of claim 1, wherein the apparatus isconfigured such that linear combiner weights obtained during a receiveoperation are capable of being used to construct linear distributionweights for a subsequent transmit operation by setting transmissiongains proportional to the distribution weights.
 38. The apparatus ofclaim 1, wherein the apparatus is configured such that the at least twosecond diverse signals include at least one cyclic prefix.
 39. Theapparatus of claim 1, wherein the processing circuitry is furthercapable of causing the apparatus to calculate weights associated withthe least two first diverse signals.
 40. The apparatus of claim 39,wherein the processing circuitry is further capable of causing theapparatus to apply the calculated weights to transmit data.
 41. Theapparatus of claim 40, wherein the processing circuitry is furthercapable of causing the apparatus to add a cyclic prefix to the transmitdata; and wherein the apparatus is configured such that the spatiallydiverse antennae array of M antennae is capable of transmitting the atleast two second diverse signals of data including the transmit data.42. The apparatus of claim 1, wherein the apparatus is configured suchthat generating the at least two second diverse signals based on the atleast one aspect of the at least two first diverse signals includesconstructing linear distribution weights based on the at least two firstdiverse signals.
 43. The apparatus of claim 1, wherein the apparatus isconfigured such that generating the at least two second diverse signalsbased on the at least one aspect of the at least two first diversesignals includes utilizing linear combiner weights obtained during areceive operation to construct linear distribution weights for asubsequent transmit operation.
 44. The apparatus of claim 1, wherein theapparatus is configured such that generating the at least two seconddiverse signals based on the at least one aspect of the at least twofirst diverse signals includes utilizing linear combiner weightsobtained during a receive operation to construct linear distributionweights for a subsequent transmit operation by setting transmissiongains proportional to the distribution weights.
 45. An apparatus,comprising: an antennae array of M antennae, where M is greater than orequal to two; at least one multiple-input-capable receiver incommunication with each antenna in the antennae array of M antennae;decoding circuitry capable of causing data to be decoded; and processingcircuitry capable of causing diversity combining, the processingcircuitry in communication with the multiple-input-capable receiver andthe decoding circuitry, the processing circuitry capable of causing theapparatus to simultaneously receive at least two first diverse signals,the at least two first diverse signals being at least one of timecoincident communications or frequency coincident communications. 46.The apparatus of claim 45, wherein the apparatus is configured such thatthe at least two diverse signals are time coincident communications. 47.The apparatus of claim 45, wherein the apparatus is configured such thatthe at least two diverse signals are frequency coincidentcommunications.
 48. An apparatus, comprising: at least two antennas; amultiple-input and multiple-output capable transceiver in communicationwith each of the at least two antennas; processing circuitry capable ofcausing diversity combining, the processing circuitry in communicationwith the multiple-input and multiple-output capable transceiver, theprocessing circuitry capable of causing the apparatus to: receive afirst signal, calculate weights associated with the first signal, andapply the weights to transmit data, and wherein the apparatus isconfigured such that the at least two antennas are capable oftransmitting a second signal including the transmit data to amultiple-input capable node.
 49. The apparatus of claim 48, wherein theapparatus is configured such that the weights are scaled to provide anormalized response associated with a water filling formula.
 50. Theapparatus of claim 48, wherein the apparatus is configured to operateutilizing at least one of a CDMA protocol, a TDMA protocol, or an SDMAprotocol.